Friday, 30 December 2016

Data Mining - Retrieving Information From Data

Data Mining - Retrieving Information From Data

Data mining definition is the process of retrieving information from data. It has become very important now days because data that is processed is usually kept for future reference and mainly for security purposes in a company. Data transforms is processed into information and it is mostly used in different ways depending on what information one is extracting and from where the person is extracting the information.

It is commonly used in marketing, scientific information and research work, fraud detection and surveillance and many more and most of this work is done using a computer. This definition can come in different terms data snooping, data fishing and data dredging all this refer to data mining but it depends in which department one is. One must know data mining definition so that he can be in a position to make data.

The method of data mining has been there for so many centuries and it is used up to date. There were early methods which were used to identify data mining there are mainly two: regression analysis and bayes theorem. These methods are never used now days because a lot of people have advanced and technology has really changed the entire system.

With the coming up or with the introduction of computers and technology, it becomes very fast and easy to save information. Computers have made work easier and one can be able to expand more knowledge about data crawling and learn on how data is stored and processed through computer science.

Computer science is a course that sharpens one skill and expands more about data crawling and the definition of what data mining means. By studying computer science one can be in a position to know: clustering, support vector machines and decision trees there are some of the units that are found on computer science.

It's all about all this and this knowledge must be applied here. Government institutions, small scale business and supermarkets use data.

The main reason most companies use data mining is because data assist in the collection of information and observations that a company goes through in their daily activity. Such information is very vital in any companies profile and needs to be checked and updated for future reference just in case something happens.

Businesses which use data crawling focus mainly on return of investments, and they are able to know whether they are making a profit or a loss within a very short period. If the company or the business is making a profit they can be in a position to give customers an offer on the product in which they are selling so that the business can be a position to make more profit in an organization, this is very vital in human resource departments it helps in identifying the character traits of a person in terms of job performance.

Most people who use this method believe that is ethically neutral. The way it is being used nowadays raises a lot of questions about security and privacy of its members. Data mining needs good data preparation which can be in a position to uncover different types of information especially those that require privacy.

A very common way in this occurs is through data aggregation.

Data aggregation is when information is retrieved from different sources and is usually put together so that one can be in a position to be analyze one by one and this helps information to be very secure. So if one is collecting data it is vital for one to know the following:

    How will one use the data that he is collecting?
    Who will mine the data and use the data.
    Is the data very secure when am out can someone come and access it.
    How can one update the data when information is needed
    If the computer crashes do I have any backup somewhere.

It is important for one to be very careful with documents which deal with company's personal information so that information cannot easily be manipulated.

source :

Saturday, 24 December 2016

One of the Main Differences Between Statistical Analysis and Data Mining

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.


Wednesday, 14 December 2016

Web Data Extraction Services

Web Data Extraction Services

Web Data Extraction from Dynamic Pages includes some of the services that may be acquired through outsourcing. It is possible to siphon information from proven websites through the use of Data Scrapping software. The information is applicable in many areas in business. It is possible to get such solutions as data collection, screen scrapping, email extractor and Web Data Mining services among others from companies providing websites such as

Data mining is common as far as outsourcing business is concerned. Many companies are outsource data mining services and companies dealing with these services can earn a lot of money, especially in the growing business regarding outsourcing and general internet business. With web data extraction, you will pull data in a structured organized format. The source of the information will even be from an unstructured or semi-structured source.

In addition, it is possible to pull data which has originally been presented in a variety of formats including PDF, HTML, and test among others. The web data extraction service therefore, provides a diversity regarding the source of information. Large scale organizations have used data extraction services where they get large amounts of data on a daily basis. It is possible for you to get high accuracy of information in an efficient manner and it is also affordable.

Web data extraction services are important when it comes to collection of data and web-based information on the internet. Data collection services are very important as far as consumer research is concerned. Research is turning out to be a very vital thing among companies today. There is need for companies to adopt various strategies that will lead to fast means of data extraction, efficient extraction of data, as well as use of organized formats and flexibility.

In addition, people will prefer software that provides flexibility as far as application is concerned. In addition, there is software that can be customized according to the needs of customers, and these will play an important role in fulfilling diverse customer needs. Companies selling the particular software therefore, need to provide such features that provide excellent customer experience.

It is possible for companies to extract emails and other communications from certain sources as far as they are valid email messages. This will be done without incurring any duplicates. You will extract emails and messages from a variety of formats for the web pages, including HTML files, text files and other formats. It is possible to carry these services in a fast reliable and in an optimal output and hence, the software providing such capability is in high demand. It can help businesses and companies quickly search contacts for the people to be sent email messages.

It is also possible to use software to sort large amount of data and extract information, in an activity termed as data mining. This way, the company will realize reduced costs and saving of time and increasing return on investment. In this practice, the company will carry out Meta data extraction, scanning data, and others as well.


Friday, 9 December 2016

Data Mining vs Screen-Scraping

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.


Monday, 5 December 2016

Three Common Methods For Web Data Extraction

Three Common Methods For Web Data Extraction

Probably the most common technique used traditionally to extract data from web pages this is to cook up some regular expressions that match the pieces you want (e.g., URL's and link titles). Our screen-scraper software actually started out as an application written in Perl for this very reason. In addition to regular expressions, you might also use some code written in something like Java or Active Server Pages to parse out larger chunks of text. Using raw regular expressions to pull out the data can be a little intimidating to the uninitiated, and can get a bit messy when a script contains a lot of them. At the same time, if you're already familiar with regular expressions, and your scraping project is relatively small, they can be a great solution.

Other techniques for getting the data out can get very sophisticated as algorithms that make use of artificial intelligence and such are applied to the page. Some programs will actually analyze the semantic content of an HTML page, then intelligently pull out the pieces that are of interest. Still other approaches deal with developing "ontologies", or hierarchical vocabularies intended to represent the content domain.

There are a number of companies (including our own) that offer commercial applications specifically intended to do screen-scraping. The applications vary quite a bit, but for medium to large-sized projects they're often a good solution. Each one will have its own learning curve, so you should plan on taking time to learn the ins and outs of a new application. Especially if you plan on doing a fair amount of screen-scraping it's probably a good idea to at least shop around for a screen-scraping application, as it will likely save you time and money in the long run.

So what's the best approach to data extraction? It really depends on what your needs are, and what resources you have at your disposal. Here are some of the pros and cons of the various approaches, as well as suggestions on when you might use each one:

Raw regular expressions and code


- If you're already familiar with regular expressions and at least one programming language, this can be a quick solution.
- Regular expressions allow for a fair amount of "fuzziness" in the matching such that minor changes to the content won't break them.
- You likely don't need to learn any new languages or tools (again, assuming you're already familiar with regular expressions and a programming language).
- Regular expressions are supported in almost all modern programming languages. Heck, even VBScript has a regular expression engine. It's also nice because the various regular expression implementations don't vary too significantly in their syntax.


- They can be complex for those that don't have a lot of experience with them. Learning regular expressions isn't like going from Perl to Java. It's more like going from Perl to XSLT, where you have to wrap your mind around a completely different way of viewing the problem.
- They're often confusing to analyze. Take a look through some of the regular expressions people have created to match something as simple as an email address and you'll see what I mean.
- If the content you're trying to match changes (e.g., they change the web page by adding a new "font" tag) you'll likely need to update your regular expressions to account for the change.
- The data discovery portion of the process (traversing various web pages to get to the page containing the data you want) will still need to be handled, and can get fairly complex if you need to deal with cookies and such.

When to use this approach: You'll most likely use straight regular expressions in screen-scraping when you have a small job you want to get done quickly. Especially if you already know regular expressions, there's no sense in getting into other tools if all you need to do is pull some news headlines off of a site.

Ontologies and artificial intelligence


- You create it once and it can more or less extract the data from any page within the content domain you're targeting.
- The data model is generally built in. For example, if you're extracting data about cars from web sites the extraction engine already knows what the make, model, and price are, so it can easily map them to existing data structures (e.g., insert the data into the correct locations in your database).
- There is relatively little long-term maintenance required. As web sites change you likely will need to do very little to your extraction engine in order to account for the changes.


- It's relatively complex to create and work with such an engine. The level of expertise required to even understand an extraction engine that uses artificial intelligence and ontologies is much higher than what is required to deal with regular expressions.
- These types of engines are expensive to build. There are commercial offerings that will give you the basis for doing this type of data extraction, but you still need to configure them to work with the specific content domain you're targeting.
- You still have to deal with the data discovery portion of the process, which may not fit as well with this approach (meaning you may have to create an entirely separate engine to handle data discovery). Data discovery is the process of crawling web sites such that you arrive at the pages where you want to extract data.

When to use this approach: Typically you'll only get into ontologies and artificial intelligence when you're planning on extracting information from a very large number of sources. It also makes sense to do this when the data you're trying to extract is in a very unstructured format (e.g., newspaper classified ads). In cases where the data is very structured (meaning there are clear labels identifying the various data fields), it may make more sense to go with regular expressions or a screen-scraping application.

Screen-scraping software


- Abstracts most of the complicated stuff away. You can do some pretty sophisticated things in most screen-scraping applications without knowing anything about regular expressions, HTTP, or cookies.
- Dramatically reduces the amount of time required to set up a site to be scraped. Once you learn a particular screen-scraping application the amount of time it requires to scrape sites vs. other methods is significantly lowered.
- Support from a commercial company. If you run into trouble while using a commercial screen-scraping application, chances are there are support forums and help lines where you can get assistance.


- The learning curve. Each screen-scraping application has its own way of going about things. This may imply learning a new scripting language in addition to familiarizing yourself with how the core application works.
- A potential cost. Most ready-to-go screen-scraping applications are commercial, so you'll likely be paying in dollars as well as time for this solution.
- A proprietary approach. Any time you use a proprietary application to solve a computing problem (and proprietary is obviously a matter of degree) you're locking yourself into using that approach. This may or may not be a big deal, but you should at least consider how well the application you're using will integrate with other software applications you currently have. For example, once the screen-scraping application has extracted the data how easy is it for you to get to that data from your own code?

When to use this approach: Screen-scraping applications vary widely in their ease-of-use, price, and suitability to tackle a broad range of scenarios. Chances are, though, that if you don't mind paying a bit, you can save yourself a significant amount of time by using one. If you're doing a quick scrape of a single page you can use just about any language with regular expressions. If you want to extract data from hundreds of web sites that are all formatted differently you're probably better off investing in a complex system that uses ontologies and/or artificial intelligence. For just about everything else, though, you may want to consider investing in an application specifically designed for screen-scraping.

As an aside, I thought I should also mention a recent project we've been involved with that has actually required a hybrid approach of two of the aforementioned methods. We're currently working on a project that deals with extracting newspaper classified ads. The data in classifieds is about as unstructured as you can get. For example, in a real estate ad the term "number of bedrooms" can be written about 25 different ways. The data extraction portion of the process is one that lends itself well to an ontologies-based approach, which is what we've done. However, we still had to handle the data discovery portion. We decided to use screen-scraper for that, and it's handling it just great. The basic process is that screen-scraper traverses the various pages of the site, pulling out raw chunks of data that constitute the classified ads. These ads then get passed to code we've written that uses ontologies in order to extract out the individual pieces we're after. Once the data has been extracted we then insert it into a database.