The characteristics of Web data determine the immense challenge for effective data mining. According to the characteristics of Web data and combining the general process of data mining (shown as figure 2), the Web data mining process can be described as the five functional modules which are shown in figure 3, namely the data acquisition, data preprocessing, data mining, analysis and evaluation and knowledge formulation modules. The functions of each module are shown as follows.

3.1 Data acquisition

In accordance with the principles of themes relating, the data acquisition module selectively obtains data from the outside Web environment to provide material and resources for the latter data mining. The data source the Web environment provided includes the Web pages data, hyperlinks data and the data of recording user visiting. According to different forms of data sources the Web data mining can be divided into content-based mining, structure -based mining and the mining based on user usage. Each data mining type may use different methods and techniques in the data acquisition process, but they have same basic process. Generally, the data acquisition is composed by three relatively independent processes which are data search, data selection and data collection.
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Figure 2 : The process of data mining
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Figure 3. : Based on Web data mining of the function 3.2 Data preprocessing

Data preprocessing module mainly processes and reconstructs the source data the data acquisition received and builds the data warehouse of related theme to create basic platform for the latter data mining process. Data preprocessing is preparations for data mining and it mainly includes data scrubbing, data integration, data conversion, data reduction, etc.

3.2.1 Data scrubbing

The main role of data scrubbing is to remove the noise and unrelated data in the source data, process the omitted data and clean the dirty data, it also includes processing the repeating data and the data without value, and completing the conversion of some data types, such as converting the same kind of information from different sources into a unified storage

3.2.2 Data integration

The main role of data integration is to handle the heterogeneous data from a number of sports environments and resolve the semantic ambiguity problem. Data integration is not a simple merger of the data, but a complex process of unified and standardized handling to the heterogeneous data.

3.2.3 Data conversion and data reduction

The main role of data conversion is to convert the data into the form which is suitable for data mining. And the main role of data reduction is to farthest reduce the data volume and enhance the efficiency of data mining algorithms through finding the useful feature of the data, and this is on the basis of fully understanding the mining tasks and the data content, under the premise of retaining the original data as possible.

3.3 Data mining

Data mining module is the core of the data mining system, and its main function is that using all kinds of data mining technologies to extract the knowledge model which is potential, effective and can be recognized by all people from the flood and pretreated data. Generally speaking, the ultimate goals of data mining only are description and prediction, the so-called description is that using comprehensible mode to express the attributes and characteristics information contained in the data; and the prediction is to find the discipline of the attributes according to their existing data value and then speculate a possible attribute value in the future. The data mining process is generally composed by three major phases: data preparation, mining operation, and interpretation of results. Data mining algorithms have certain on data, such as small redundancy, small correlation between the data attributes, small error ratio. But the data actually collected usually is chaotic, redundant and half-baked, so data mining must has the stage of data preparation for improving the quality of it. Mining operations include the choice of appropriate algorithms, operations of mining knowledge, last the operations of confirming knowledge; expression and interpretation stage is to analyze the results, extract the most valuable information. If the information obtained cannot make the decision -makers satisfied, the above data mining stage is needed to repeat.

3.4 Analysis and evaluation

Analysis and evaluation module is to analyze the credibility and effectiveness of the knowledge mode the data mining obtained, and educe evaluate conclusions to provide information support for the management decision-making of users.

3.5 Knowledge formulation

Knowledge expression module refers to the knowledge modes mined from the Web data by using data mining tools, and it will be shown with appropriate form to facilitate user acceptance and mutual exchange.

Representative APR 391%

Let's say you want to borrow $100 for two week. Lender can charge you $15 for borrowing $100 for two weeks. You will need to return $115 to the lender at the end of 2 weeks. The cost of the $100 loan is a $15 finance charge and an annual percentage rate of 391 percent. If you decide to roll over the loan for another two weeks, lender can charge you another $15. If you roll-over the loan three times, the finance charge would climb to $60 to borrow the $100.

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