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Web mining is the use of data mining techniques to automatically discover and extract information from World Wide Web documents and services. This article considers the question: is effective Web mining possible? Skeptics believe that the Web is too unstructured for Web mining to succeed. Indeed, data mining has been applied to databases traditionally, yet much of the information on the Web lies buried in documents designed for human consumption such as home pages or product catalogs. Furthermore, much of the information on the Web is presented in natural language text with no machine-readable semantics; HTML annotations structure the display of Web pages, but provide little insight into their content. Some have advocated transforming the Web into a massive layered database to facilitate data mining, but the Web is too dynamic and chaotic to be tamed in this manner.

Recently, the web is becoming an important part of people’s life. The web is a very good place to run successful businesses. Selling products or services online plays an important role in the success of businesses that have a physical presence, like a retail business. Therefore, it is important to have a successful website to serve as a sales and marketing tool. One of the effective used technologies for that purpose is data mining. Data mining is the process of extracting interesting patterns from large databases. Web mining is the usage of data mining techniques to extract interesting information from web data. This paper presents the three components of web mining: web usage mining, web structure mining and web content mining and the main data preprocessing tasks for web usage mining

Recently, the web is becoming an important part of people’s life. The web is a very good place to run successful businesses. Selling products or services online plays an important role in the success of businesses that have a physical presence, like a retail business. Therefore, it is important to have a successful website to serve as a sales and marketing tool. One of the effective used technologies for that purpose is data mining. Data mining is the process of extracting interesting patterns from large databases. Web mining is the usage of data mining techniques to extract interesting information from web data. This paper presents the three components of web mining: web usage mining, web structure mining and web content mining and the main data preprocessing tasks for web usage mining.

The first step in the Web usage mining process consists of collecting the relevant Web data, which will be analyzed to provide useful information about the users’ behavior. There are two main sources of data for Web usage mining, corresponding to the two software systems interacting during a Web session: data on the Web server side and data on the client side. In addition, when intermediaries are introduced in the client–server communication, they can also become sources for usage data, like proxy servers and packet sniffers. We will consider each of these sources in the following paper. Also we are trying to associate the data collection methods with the requirements imposed by different classes of personalization functions.

Data collected from Web in the first stage of data mining are usually diverse and voluminous. These data must be assembled into a consistent, integrated and comprehensive view, in order to be used for pattern discovery. Like in most applications of data mining, data preprocessing involves removing and filtering redundant and irrelevant data, predicting and filling in missing values, removing noise, transforming and encoding data, as well as resolving any inconsistencies. The task of data transformation and encoding is particularly important for the success of data mining.