Data Mining Tools - Understanding Data Mining

Data mining basically means pulling out important information from huge volume of data. Data mining tools are used for the purposes of examining the data from various viewpoints and summarizing it into a useful database library. However, lately these tools have become computer based applications in order to handle the growing amount of data. They are also sometimes referred to as knowledge discovery tools.

As a concept, data mining has always existed since the past and manual processes were used as data mining tools. Later with the advent of fast processing computers, analytical software tools, and increased storage capacities automated tools were developed, which drastically improved the accuracy of analysis, data mining speed, and also brought down the costs of operation. These methods of data mining are essentially employed to facilitate following major elements:

    Pull out, convert, and load data to a data warehouse system
    Collect and handle the data in a database system
    Allow the concerned personnel to retrieve the data
    Data analysis
    Data presentation in a format that can be easily interpreted for further decision making

We use these methods of mining data to explore the correlations, associations, and trends in the stored data that are generally based on the following types of relationships:

    Associations - simple relationships between the data
    Clusters - logical correlations are used to categorise the collected data
    Classes - certain predefined groups are drawn out and then data within the stored information is searched based on these groups
    Sequential patterns - this helps to predict a particular behavior based on the trends observed in the stored data

Industries which cater heavily to consumers in retail, financial, entertainment, sports, hospitality and so on rely on these data methods of obtaining fast answers to questions to improve their business. The tools help them to study to the buying patterns of their consumers and hence plan a strategy for the future to improve sales. For e.g. restaurant might want to study the eating habits of their consumers at various times during the day. The data would then help them in deciding on the menu at different times of the day. Data mining tools certainly help a great deal when drawing out business plans, advertising strategies, discount plans, and so on. Some important factors to consider when selecting a data mining tool include the platforms supported, algorithms on which they work (neural networks, decisions trees), input and output options for data, database structure and storage required, usability and ease of operation, automation processes, and reporting methods.


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Data Discovery vs. Data Extraction

Looking at screen-scraping at a simplified level, there are two primary stages involved: data discovery and data extraction. Data discovery deals with navigating a web site to arrive at the pages containing the data you want, and data extraction deals with actually pulling that data off of those pages. Generally when people think of screen-scraping they focus on the data extraction portion of the process, but my experience has been that data discovery is often the more difficult of the two.

The data discovery step in screen-scraping might be as simple as requesting a single URL. For example, you might just need to go to the home page of a site and extract out the latest news headlines. On the other side of the spectrum, data discovery may involve logging in to a web site, traversing a series of pages in order to get needed cookies, submitting a POST request on a search form, traversing through search results pages, and finally following all of the "details" links within the search results pages to get to the data you're actually after. In cases of the former a simple Perl script would often work just fine. For anything much more complex than that, though, a commercial screen-scraping tool can be an incredible time-saver. Especially for sites that require logging in, writing code to handle screen-scraping can be a nightmare when it comes to dealing with cookies and such.

In the data extraction phase you've already arrived at the page containing the data you're interested in, and you now need to pull it out of the HTML. Traditionally this has typically involved creating a series of regular expressions that match the pieces of the page you want (e.g., URL's and link titles). Regular expressions can be a bit complex to deal with, so most screen-scraping applications will hide these details from you, even though they may use regular expressions behind the scenes.

As an addendum, I should probably mention a third phase that is often ignored, and that is, what do you do with the data once you've extracted it? Common examples include writing the data to a CSV or XML file, or saving it to a database. In the case of a live web site you might even scrape the information and display it in the user's web browser in real-time. When shopping around for a screen-scraping tool you should make sure that it gives you the flexibility you need to work with the data once it's been extracted.


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Internet Outsourcing Data Entry to Third World Countries

Outsourcing pieces of your company is cost effective. The economic downturn has made companies explore more fiscally conservative options for their company. Internet outsourcing is one of the most popular options to effectively cut costs. Entire departments that cost companies millions a year can be shipped overseas. This allows companies to focus their resources on the crucial elements of their company and not use resources on trivial but necessary matters.

One of the most common departments outsourced is customer service. Maintaining a customer service department requires health benefits, rent, and costly salaries. This creates a huge expense for a company for simple tasks. Customer service departments are being outsourced to India and China for a fraction of the cost. Customer service often requires a straightforward question and answer script. The answers can be given to anyone who has the script. This makes outsourcing customer service effective.

If someone calls for customer support and the customer service representative answers the phone and does not know the answer there is a solution. Calls can be transferred to customer representatives that have extensive product knowledge. This elite group of customer service representatives can be located at corporate headquarters or can be transferred to a trained group of outsourced customer representatives that have knowledge beyond the script. This is one of the easiest ways to cut costs and maintain the value of the company. Over 90% of customer support questions are repeat questions that can be scripted.

Data entry is one the most common outsourced departments. People who do not speak the same language as the origin country can often do data entry tasks. This makes outsourcing data entry extremely cost effective. Numbers and symbols are universal making data entry straightforward in most foreign countries.

All outsourcing tasks can be distributed online. Internet outsourcing is the future to big and small businesses creating cost effective business plans. Placing an order online for electronic equipment has become a normal way of shopping. Placing online orders for work will be common in the decades to come.

Companies worry about outsourcing because they're concerned about quality. Outsourcing has become big business in China, India, third world and developing countries. Projects outsourced are taken very seriously and business management is similar to western societies. The regulations are often more strict than the United States and the work is often held to a higher standard to insure repeat business.



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Online Data Entry Projects - Grab An Online Audience by Data Entry

In recent scenario, it is very tough to grab the business from every angle. You must require a very huge marketing budget as well as setup to mange the marketing teams. One of the biggest sources is online audience. You must grab their attention to generate more value.

It is not so hard to grab attention of online customers but you require more time and some person to work on. Rather doing this process at own, I suggest outsourcing the projects as online data entry. That will be surly beneficial for your company. This way, you can get out of time consuming and tedious task. Here is the small list of online data entry projects that can help you in developing reputation of your business:

Twitter Status Update:
Twitter is the most famous online community. Some of you may know as micro-blogging site. It is used to connect with peoples and exchanging thoughts. People join in but not have an idea what to post. To capture more and more attention, you consistently have to update the status. You can outsource online data entry project and get good business without spending time on it. My friend managing a small business of duct cleaning said "140 characters make much difference in your profit".

Articles:
Articles are one of the oldest sources of getting new clients and generating business from online source. This is the platform where you can give more information or knowledge about your services. You can describe the benefits, usefulness of product. You have to write about 250 to 350 words and to submit in article directories. This is out of focus task for you, so outsourcing as online data entry project is the best method. Matthew working in insurance firm told me "I am getting lot of customer from articles. If I am not getting help of articles, I am sure about kick-out from this job".

Blog Post Entries:
A blog is a personal place where you can share the latest updates and detailed information about your business. Various companies and individual has blogs but they are unable to mange those. Through online data entry, you can get professionals who can easily mange your blogs. My colleague is managing blog and getting 2000 visitor in just six months. So, outsource the online data typing projects and move your business a step ahead of competitor.

Bea Arthur is a quality controller at Data Entry India, a well-known firm, accepting data entry projects, data conversion projects and data processing projects. They are having more than 17 years of experience in online data entry



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One of the Main Differences Between Statistical Analysis and Data Mining

Two methods of analyzing data that are common in both academic and commercial fields are statistical analysis and data mining. While statistical analysis has a long scientific history, data mining is a more recent method of data analysis that has arisen from Computer Science. In this article I want to give an introduction to these methods and outline what I believe is one of the main differences between the two fields of analysis.

Statistical analysis commonly involves an analyst formulating a hypothesis and then testing the validity of this hypothesis by running statistical tests on data that may have been collected for the purpose. For example, if an analyst was studying the relationship between income level and the ability to get a loan, the analyst may hypothesis that there will be a correlation between income level and the amount of credit someone may qualify for.

The analyst could then test this hypothesis with the use of a data set that contains a number of people along with their income levels and the credit available to them. A test could be run that indicates for example that there may be a high degree of confidence that there is indeed a correlation between income and available credit. The main point here is that the analyst has formulated a hypothesis and then used a statistical test along with a data set to provide evidence in support or against that hypothesis.

Data mining is another area of data analysis that has arisen more recently from computer science that has a number of differences to traditional statistical analysis. Firstly, many data mining techniques are designed to be applied to very large data sets, while statistical analysis techniques are often designed to form evidence in support or against a hypothesis from a more limited set of data.

Probably the mist significant difference here, however, is that data mining techniques are not used so much to form confidence in a hypothesis, but rather extract unknown relationships may be present in the data set. This is probably best illustrated with an example. Rather than in the above case where a statistician may form a hypothesis between income levels and an applicants ability to get a loan, in data mining, there is not typically an initial hypothesis. A data mining analyst may have a large data set on loans that have been given to people along with demographic information of these people such as their income level, their age, any existing debts they have and if they have ever defaulted on a loan before.

A data mining technique may then search through this large data set and extract a previously unknown relationship between income levels, peoples existing debt and their ability to get a loan.

While there are quite a few differences between statistical analysis and data mining, I believe this difference is at the heart of the issue. A lot of statistical analysis is about analyzing data to either form confidence for or against a stated hypothesis while data mining is often more about applying an algorithm to a data set to extract previously unforeseen relationships.


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Business Intelligence Data Mining

Data mining can be technically defined as the automated extraction of hidden information from large databases for predictive analysis. In other words, it is the retrieval of useful information from large masses of data, which is also presented in an analyzed form for specific decision-making.

Data mining requires the use of mathematical algorithms and statistical techniques integrated with software tools. The final product is an easy-to-use software package that can be used even by non-mathematicians to effectively analyze the data they have. Data Mining is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, fraud detection, web site personalization, e-commerce, healthcare, customer relationship management, financial services and telecommunications.

Business intelligence data mining is used in market research, industry research, and for competitor analysis. It has applications in major industries like direct marketing, e-commerce, customer relationship management, healthcare, the oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities. BI uses various technologies like data mining, scorecarding, data warehouses, text mining, decision support systems, executive information systems, management information systems and geographic information systems for analyzing useful information for business decision making.

Business intelligence is a broader arena of decision-making that uses data mining as one of the tools. In fact, the use of data mining in BI makes the data more relevant in application. There are several kinds of data mining: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining, that are all used in business intelligence applications.

Some data mining tools used in BI are: decision trees, information gain, probability, probability density functions, Gaussians, maximum likelihood estimation, Gaussian Baves classification, cross-validation, neural networks, instance-based learning /case-based/ memory-based/non-parametric, regression algorithms, Bayesian networks, Gaussian mixture models, K-means and hierarchical clustering, Markov models and so on.


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Innovative Online Data Entry Services

Number of companies providing data entry services has increased in the last few years. These companies also provide services on online and offline data-entry and data processing, etc. Data Entry is to enter any form of data into computerized inventory. It could be done by typing at a keyboard plus electronically entering information into the machine.

These companies have updated technologies, unique processes and efficient data processing by integrating skilled professionals. These companies deliver high-quality services with complete accuracy, efficiency plus effectiveness. They provide services through reliable and secure online platform with the help of encrypted FTP upload CD-R or CD-W or E-mail. Adopting this technology customers get an assurance that their information is free from any sort of unauthorized access, copying or downloading. Companies specializing in such services provide a broad spectrum of services fulfilling each customer specific needs.

Few of these services are listed as follows: surveys, online copying, pasting, sorting, editing, and organizing data, questionnaires, online form processing and filing, reports and submissions, online medical and legal data entry, data collection, mailing list / mailing label, email mining, typing the manuscript in MS Word, etc. Outsourcing of the documentation of the work is a workable and a reasonable option.

Such services includes a wide range of back office and BPO - Business Process Outsourcing and ITO - Information Technology Outsourcing enabled data processing services.

Online data input services provided by India have earned a global recognition for its superior quality and timely completion of its work. Saving time is crucial for each organization running its business. Qualitative output is produced in lesser time which is advantageous for using the time at other important places. By availing such services one can save on cost of hiring trained professionals. More services could be availed within the saved cost.

Talking about the role of online data processing services, as the requirements of high quality and accurate data-entry of textual and numeric data processing business needs is most needed. In this way, companies can save valuable time and money by entering information online reduces. You can also consult experts who have vast experience and knowledge about online entry of data.

With the help of these services, mostly many business processing companies are able to focus on their core activates through online services. This kind of services require speed, analytical skills, domain expertise and industry experience. Choosing right outsourcing partner can save you cost and time significantly.


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Data Entry Services in India Are Getting Famous in the World!

Outsourcing has become the most profitable business in the world. This business is growing in India and other part of the world. These services are getting famous in the world and most of the business owners are saving their lots of money by doing outsourcing to different countries where India comes in top in the outsourcing. By outsourcing your offline and online information entry jobs, your company will maintain properly organized and up-to-date records of the employees and other important stuff. These jobs are usually done in the home environment.

India is very popular in providing the BPO services for their customers. There is large scale of BPO service providers running their business in India. The employees working in these offices are also very competent and trained. Data entry services in India is very popular all around the world because of having the access of BPO experts and the web data extraction experts.

What these BPO services provide you?

There are many business across the globe running on the outsource services, BPO services in India provides the ease of life to the business owner want quick and fast data entry work.

There are many well reputed firms working in India and doing their best to finish and deliver comes punctually. They're professional well equipped with the newest technology and software and more importantly with the professional labor work. They are fully trained and expert in their niche so if a business owner take the services then they get the in time work and quality. When you will select any BPO expert then you will find the following data entry expertise in these professional companies.

1. You will find the handwritten material with the help of experts.
2. Knowledge entry of e-books, directories, image files and etc.
3. You will also get the best services of data processing.
4. Business card knowledge entry
5. Bills and survey services which will help you to Maintain and correct records.
6. Alpha numeric data entry services
7. Data entry free trails.

Thousand of online BPO jobs are also available on the Indian big job portals and other data entry work. These services and work force reduce your workload and will enhance your productivity of your business. Outsourcing the right choice by any business owner because it reduces your total cost and you get the perfect and reliable work. When you approach to any professional service provider firm in India then it reduce the turnaround time and you get the professional data entry services.

Accurate, fast and reliable services are offered in India by the Bpo companies. Please visit Data Entry Services for more information.


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How Do We Store Data for Future Data Mining Without Knowing the Future Questions?

Let's talk a little bit about "transparency versus public access" and where it's appropriate, and where it obviously isn't. Not long ago, there was an interesting feature in the TV news, a big to do about nothing, where the First Lady Michelle had traveled to Spain, and as she was on her vacation, she was on vacation as a private citizen. Now whereas, people want transparency, one has to ask where privacy must take precedent, and where transparency should be afforded.

Now, you might not think this is a very good example, but when it comes to online social networks, paparazzi, and privacy all these things are really big issues. Recall when Sarah Palin's yahoo email account was hacked by a college student, Obama supporter in TN? Obviously, that crossed the line, but where do we draw the line online?

Okay so, let's get back to the main question here; How Do We Store Online Data without violating personal property, and how do we protect national security without breaches in data, or violations of personal privacy. And if we anonimize all the data for use at a future time, how should we store it for Future Data Mining Without Knowing the Future Questions?

The information and data could be stored by region, time, frequency, and relevance. It must be stored for a multitude of purposes, and we must determine who may obtain the data, who will use the data, and what will they use it for. You see, there are different ways to store the information categories to be displayed in, or various types of tags to assign it to.

Perhaps, all the information can be stored, every bit of it, and a trusted data inquirer who wants to ask the questions, will have to explain their inquiry to an artificially intelligent computer, and it can act like a Supreme Court review on privacy. In other words, if the reason for the information is not good enough, access to that particular information will be denied. And yes it could use constitutional extrapolations, which would be philosophically based on the same analogy as surgeon seizure rules, or Fifth Amendment rights of self-determination.

As if the data itself would be alive, and the artificial intelligent computer would be the judge deciding if the prosecution would be allowed to ask those questions of the computer data system. In this case you could just store all the information you could possibly take in, and not worry about it. Okay so, that is one option; just store all the data, regardless of what it is. Or another option is to store only some data, data you believe to be important for the future, but knowing the whole truth of the past, is not completely known.

This is problematic however due to "selective prosecution" challenges. You see, one of my biggest fears would be information taken at a context, and used to condemn people or character assassinate them, or incriminate them at a trial, or in the mass media in court of public opinion using stored data, using a computer forensic chain of data, selectively gathered.

We know that the media uses this trick early and often, and they do so in often ruining people's lives. We need to be careful with that. It's serious issue. The reality is you cannot trust humans, they have proven throughout history to be a trustworthy, and you don't have to go very far to find inherent corruptness and individuals of the human species. This being my primary reason for suggesting an AI computer system.

The other concept might be to not collect the data at all, because you don't really need the data, and if you have the data available, we all know that it will be abused. Of course, the proof of innocence could also very well be in that same data, you see that point? But, the chances for abuse is far too great when humans are involved. We've had previous Presidential Administrations use IRS data to attack their enemies, and use the FBI to track political opponents. State Governors have used state police to track persons whom they've had disputes with or political adversaries as well. The abuse of power is quite common.

So, under the opposite model, you could say; No Data from Anyone, Agency, Corporation, or Organization maybe collected period; you can't collect it, you can't have it, and you can't use it. That means you can't use it for good or for evil. Some might say that would be unfortunate because a lot of that data can help prevent crimes, it can help better solve the challenges and problems of our society, and it can help artificial intelligence make the best decisions based on the best information.

If we continually make decisions based on lack of information, is this really a smart way to do planning? If on the other hand we have irrelevant information, bad information, or information taken out of context, we will never be able to make any decisions without very unfortunate unintended consequences, which is what is happening now it seems.

At our think tank we talk a lot about this, but we don't do political correctness, and we aren't about to give the human species a free pass on integrity, they don't deserve it, they haven't earned it, and we all know they cannot be trusted.


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Web Data Extraction

The Internet as we know today is a repository of information that can be accessed across geographical societies. In just over two decades, the Web has moved from a university curiosity to a fundamental research, marketing and communications vehicle that impinges upon the everyday life of most people in all over the world. It is accessed by over 16% of the population of the world spanning over 233 countries.

As the amount of information on the Web grows, that information becomes ever harder to keep track of and use. Compounding the matter is this information is spread over billions of Web pages, each with its own independent structure and format. So how do you find the information you're looking for in a useful format - and do it quickly and easily without breaking the bank?

Search Isn't Enough

Search engines are a big help, but they can do only part of the work, and they are hard-pressed to keep up with daily changes. For all the power of Google and its kin, all that search engines can do is locate information and point to it. They go only two or three levels deep into a Web site to find information and then return URLs. Search Engines cannot retrieve information from deep-web, information that is available only after filling in some sort of registration form and logging, and store it in a desirable format. In order to save the information in a desirable format or a particular application, after using the search engine to locate data, you still have to do the following tasks to capture the information you need:

· Scan the content until you find the information.

· Mark the information (usually by highlighting with a mouse).

· Switch to another application (such as a spreadsheet, database or word processor).

· Paste the information into that application.

Its not all copy and paste

Consider the scenario of a company is looking to build up an email marketing list of over 100,000 thousand names and email addresses from a public group. It will take up over 28 man-hours if the person manages to copy and paste the Name and Email in 1 second, translating to over $500 in wages only, not to mention the other costs associated with it. Time involved in copying a record is directly proportion to the number of fields of data that has to copy/pasted.

Is there any Alternative to copy-paste?

A better solution, especially for companies that are aiming to exploit a broad swath of data about markets or competitors available on the Internet, lies with usage of custom Web harvesting software and tools.

Web harvesting software automatically extracts information from the Web and picks up where search engines leave off, doing the work the search engine can't. Extraction tools automate the reading, the copying and pasting necessary to collect information for further use. The software mimics the human interaction with the website and gathers data in a manner as if the website is being browsed. Web Harvesting software only navigate the website to locate, filter and copy the required data at much higher speeds that is humanly possible. Advanced software even able to browse the website and gather data silently without leaving the footprints of access.

The next article of this series will give more details about how such softwares and uncover some myths on web harvesting.


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Reduce Your Burden with Data Entry Services

In this fast paced business world, it is highly competitive for a business to sustain in the market with the consistent rate of growth. Every aspect of a business firm demands heavy attention and intelligent effort to give quality performance. One such crucial element of a profit making firm is data entry. The task is of entering data is not only time consuming but also requires a great deal of concentration and efficiency. Thankfully, the facility of data entry services has made the assistance of data entry, easily approachable and feasible. The entire set of data entry services carry out several official activities such as documentation, image enhancement, processing, photo manipulation, data conversion and many more.

Every transaction must be filed, processed and analyzed with a sincere effort, as they are responsible for estimating the profits and losses the company. The financial statements are finally created on the basis of these data entries that in turn decide the standard of the organization in the market. However, this is not all; the data of any company is also used be several third party partners including shareholders, creditors, consumers, employees of the company. Therefore, the entire process of data entry services gives the clear picture of the present and future prospects of the company and hence best described as a vital aspect for any organization. And for this reason, many outsourcing companies are making sincere contribution in providing data entry services to all business organizations.

Through the assistance of data entry services, these companies save a lot on their internal resources such as financial expense and staff recruitment. These service providers are well recruited with efficient employees, who are highly qualified and well trained to carry out the entire job of entering data. With the advent of data entry outsourcing, many companies have successfully reduced their cost of expense as they have the privilege of relegating their huge salaried staff, which earlier they were compelled to hire for accurate data entry procedure. If considered on monetary level, the service provider charges a very reasonable amount, which is much lower than the in-house staff in comparison, as the owners do not have to pay any added allowances and bonuses.

There are firms that do not require the data entry to be performed on a regular basis. For such companies the option of data entry services is very appropriate as they get the leniency of hiring the professionals on contractual basis according to the projects. The charges are quoted in accordance with the volume of the work offered. On the other hand the companies with routine data entry can also take assistance from such services, as they are cheap and offer quality work with perfect time management. However, the task of entry data is very demanding and time consuming. It requires serious input of all the transaction because one minute mistake or a single wrong entry can result into huge statistical problem and total miscalculation. A company cannot afford to deal through this kind of error, as the entire reputation of the business organization is estimated through the data entries.


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Online Data Entry and Data Mining Services

Data entry job involves transcribing a particular type of data into some other form. It can be either online or offline. The input data may include printed documents like Application forms, survey forms, registration forms, handwritten documents etc.

Data entry process is an inevitable part of the job to any organization. One way or other each organization demands data entry. Data entry skills vary depends upon the nature of the job requirement, in some cases data to be entered from a hard copy formats and in some other cases data to be entered directly into a web portal. Online data entry job generally requires the data to be entered in to any online data base.

For a super market, data associate might be required to enter the goods which have sold in a particular day and the new goods received in a particular day to maintain the stock well in order. Also, by doing this the concerned authorities will get an idea about the sale particulars of each commodity as they requires. In another example, an office the account executive might be required to input the day to day expenses in to the online accounting database in order to keep the account well in order.

The aim of the data mining process is to collect the information from reliable online sources as per the requirement of the customer and convert it to a structured format for the further use. The major source of data mining is any of the internet search engine like Google, Yahoo, Bing, AOL, MSN etc. Many search engines such as Google and Bing provide customized results based on the user's activity history. Based on our keyword search, the search engine lists the details of the websites from where we can gather the details as per our requirement.

Collect the data from the online sources such as Company Name, Contact Person, Profile of the Company, Contact Phone Number of Email ID Etc. are doing for the marketing activities. Once the data is gathered from the online sources into a structured format, the marketing authorities will start their marketing promotions by calling or emailing the concerned persons, which may result to create a new customer. So basically data mining is playing a vital role in today's business expansions. By outsourcing the data entry and its related works, you can save the cost that would be incurred in setting up the necessary infrastructure and employee cost.


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Data Mining vs Screen-Scraping

Data mining isn't screen-scraping. I know that some people in the room may disagree with that statement, but they're actually two almost completely different concepts.

In a nutshell, you might state it this way: screen-scraping allows you to get information, where data mining allows you to analyze information. That's a pretty big simplification, so I'll elaborate a bit.

The term "screen-scraping" comes from the old mainframe terminal days where people worked on computers with green and black screens containing only text. Screen-scraping was used to extract characters from the screens so that they could be analyzed. Fast-forwarding to the web world of today, screen-scraping now most commonly refers to extracting information from web sites. That is, computer programs can "crawl" or "spider" through web sites, pulling out data. People often do this to build things like comparison shopping engines, archive web pages, or simply download text to a spreadsheet so that it can be filtered and analyzed.

Data mining, on the other hand, is defined by Wikipedia as the "practice of automatically searching large stores of data for patterns." In other words, you already have the data, and you're now analyzing it to learn useful things about it. Data mining often involves lots of complex algorithms based on statistical methods. It has nothing to do with how you got the data in the first place. In data mining you only care about analyzing what's already there.

The difficulty is that people who don't know the term "screen-scraping" will try Googling for anything that resembles it. We include a number of these terms on our web site to help such folks; for example, we created pages entitled Text Data Mining, Automated Data Collection, Web Site Data Extraction, and even Web Site Ripper (I suppose "scraping" is sort of like "ripping"). So it presents a bit of a problem-we don't necessarily want to perpetuate a misconception (i.e., screen-scraping = data mining), but we also have to use terminology that people will actually use.



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Collecting Data With Web Scrapers

There is a large amount of data available only through websites. However, as many people have found out, trying to copy data into a usable database or spreadsheet directly out of a website can be a tiring process. Data entry from internet sources can quickly become cost prohibitive as the required hours add up. Clearly, an automated method for collating information from HTML-based sites can offer huge management cost savings.

Web scrapers are programs that are able to aggregate information from the internet. They are capable of navigating the web, assessing the contents of a site, and then pulling data points and placing them into a structured, working database or spreadsheet. Many companies and services will use programs to web scrape, such as comparing prices, performing online research, or tracking changes to online content.

Let's take a look at how web scrapers can aid data collection and management for a variety of purposes.

Improving On Manual Entry Methods

Using a computer's copy and paste function or simply typing text from a site is extremely inefficient and costly. Web scrapers are able to navigate through a series of websites, make decisions on what is important data, and then copy the info into a structured database, spreadsheet, or other program. Software packages include the ability to record macros by having a user perform a routine once and then have the computer remember and automate those actions. Every user can effectively act as their own programmer to expand the capabilities to process websites. These applications can also interface with databases in order to automatically manage information as it is pulled from a website.

Aggregating Information

There are a number of instances where material stored in websites can be manipulated and stored. For example, a clothing company that is looking to bring their line of apparel to retailers can go online for the contact information of retailers in their area and then present that information to sales personnel to generate leads. Many businesses can perform market research on prices and product availability by analyzing online catalogues.

Data Management

Managing figures and numbers is best done through spreadsheets and databases; however, information on a website formatted with HTML is not readily accessible for such purposes. While websites are excellent for displaying facts and figures, they fall short when they need to be analyzed, sorted, or otherwise manipulated. Ultimately, web scrapers are able to take the output that is intended for display to a person and change it to numbers that can be used by a computer. Furthermore, by automating this process with software applications and macros, entry costs are severely reduced.

This type of data management is also effective at merging different information sources. If a company were to purchase research or statistical information, it could be scraped in order to format the information into a database. This is also highly effective at taking a legacy system's contents and incorporating them into today's systems.

Overall, a web scraper is a cost effective user tool for data manipulation and management.


Source: http://ezinearticles.com/?Collecting-Data-With-Web-Scrapers&id=4223877

Data Mining As a Process

The data mining process is also known as knowledge discovery. It can be defined as the process of analyzing data from different perspectives and then summarizing the data into useful information in order to improve the revenue and cut the costs. The process enables categorization of data and the summary of the relationships is identified. When viewed in technical terms, the process can be defined as finding correlations or patterns in large relational databases. In this article, we look at how data mining works its innovations, the needed technological infrastructures and the tools such as phone validation.

Data mining is a relatively new term used in the data collection field. The process is very old but has evolved over the time. Companies have been able to use computers to shift over the large amounts of data for many years. The process has been used widely by the marketing firms in conducting market research. Through analysis, it is possible to define the regularity of customers shopping. How the items are bought. It is also possible to collect information needed for the establishment of revenue increase platform. Nowadays, what aides the process is the affordable and easy disk storage, computer processing power and applications developed.

Data extraction is commonly used by the companies that are after maintaining a stronger customer focus no matter where they are engaged. Most companies are engaged in retail, marketing, finance or communication. Through this process, it is possible to determine the different relationships between the varying factors. The varying factors include staffing, product positioning, pricing, social demographics, and market competition.

A data-mining program can be used. It is important note that the data mining applications vary in types. Some of the types include machine learning, statistical, and neural networks. The program is interested in any of the following four types of relationships: clusters (in this case the data is grouped in relation to the consumer preferences or logical relationships), classes (in this the data is stored and finds its use in the location of data in the per-determined groups), sequential patterns (in this case the data is used to estimate the behavioral patterns and patterns), and associations (data is used to identify associations).

In knowledge discovery, there are different levels of data analysis and they include genetic algorithms, artificial neural networks, nearest neighbor method, data visualization, decision trees, and rule induction. The level of analysis used depends on the data that is visualized and the output needed.

Nowadays, data extraction programs are readily available in different sizes from PC platforms, mainframe, and client/server. In the enterprise-wide uses, size ranges from the 10 GB to more than 11 TB. It is important to note that two crucial technological drivers are needed and are query complexity and, database size. When more data is needed to be processed and maintained, then a more powerful system is needed that can handle complex and greater queries.


Source: http://ezinearticles.com/?Data-Mining-As-a-Process&id=7181033