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Restrictions And Challenges In Web Data Mining Process

Posted On : Nov-22-2011 | seen (240) times | Article Word Count : 555 |

Today's World Wide Web is flooded with billions of web pages created using static and dynamic programming languages such as HTML, PHP and ASP. Web is a great source of information that a lush playground for data mining.
Today's World Wide Web is flooded with billions of web pages created using static and dynamic programming languages such as HTML, PHP and ASP. Web is a great source of information that a lush playground for data mining. Because the data is stored on the Web in various formats and are dynamic in nature, is a major challenge for research, processing and presenting unstructured information available on the web.

Complexity of a web page is much larger than the complexity of each document conventional text. Web pages on the Internet without uniformity and standardization, while traditional books and text documents are much simpler in their consistency. In addition, the search engines with limited capacity can not index all web pages which data mining is extremely inefficient.

The Internet is a source of knowledge is very dynamic and growing at a rapid pace. Sports, news, finance, and corporate sites to update their sites at one hour or per day basis. Now millions of Web users achieved with different profiles, interests and purpose of use. Each of these requires good information, but do not know how relevant data efficiently and with little effort to retrieve.

It is important to note that only a small part of the web truly useful information. There are three common methods for the user in accessing the information stored on the Internet:

1. Use general keywords or major search engines lead to millions of Web pages, many of which are totally irrelevant.

2. The semantics similar keyword or multi-variant return my results ambiguous. For an instant word panther is an animal, sports accessories, or the name of the movie.

3. It is possible that you can miss a lot of highly relevant web pages that are not directly under the keyword.

To use the web as an effective tool and knowledge discovery researchers have developed effective techniques for data mining to easily retrieve the relevant data, smoothly and
Profitably.

Web data mining and data collection process is critical for many companies and market research today. Conventional techniques of data mining on Web search engines like Google, Yahoo, AOL, etc. and keywords, directories and themes. Because the existing structure of the web can not provide information of high quality, accurate and intelligent, systematic Web mining can help you get the desired business intelligence and data.

The main factor that prevents access deep web is the effectiveness of the search engine robots. Modern search engine robots or bots can not access the entire Web because of bandwidth limitations. There are thousands of Internet databases with high quality and well maintained scanned publisher can provide information, but can not be opened by the crawlers.

Almost all search engines have few opportunities to combine keyword search. Such as Google and Yahoo offer as an optional phrase or exact match to narrow your search. It takes more effort and time to more relevant information. Because human behavior and the choices change over time, a regularly updated website to reflect these trends.

There is limited space for the web of multi-dimensional data mining for information retrieval are highly dependent on the existing keyword-based indices, not actual data. Above limitations and challenges have led to a search efficiently and effectively discover and use Web resources.

Article Source : http://www.articleseen.com/Article_Restrictions And Challenges In Web Data Mining Process_108227.aspx

Author Resource :
Joseph Hayden writes article on Web Data Mining, Website Data Mining, Data Mining Services, Web Screen Scraping, Web Data Scraping, Web Data Extraction etc.

Keywords : Website Data Scraping, Data Mining Services, Web Data Extraction,

Category : Business : Small Business

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