Web usage mining research papers 2011

Yu-Hui Tao proposed a unified intention-based Web transaction mining algorithm that can efficiently process the whole data set simultaneously with multiple intentional browsing data types as well as transform the intentional browsing data counts into easily understood linguistic items using the fuzzy set concept [6]. Enrique proposed a new method towards automatic personalized recommendation based on the behavior of a single user in accordance with all other users in web-based information systems by algorithm Apriori [7].

Jianping Zeng proposed a framework which can be easily implemented for the analysis of user activity on an interactive website, in the framework, user activity model is represented by a hidden Markov model HMM , and the method for user interest computation is provided [8]. The algorithm Apriori is the most commonly used in mining association rules, it can be embed in weka, so the association of web log can be analyzed on the platform weka to discover the visit pattern and other information of user. Weka is approved widely and is one of most complete tools in data mining.

As a public data mining platform, weka gathers many machine learning algorithms to mine data, including data pretreatment, classification, regression, cluster class, association rule mining and visualization on new interface. Weka has used a series of unified graphical interface with standard machine learning technology, it may unify many methods of pretreatments and post-processing, many different study algorithms are applied in data set and appraise the corresponding result.

For example, weka 3. Web server log is the main data source for web log mining that records the user's request information to server.

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The log form is expansion, each record contains request data, request time, IP address of client, service name, server name, IP address of server, server port, method, request page URL, protocol state, sending bytes, receiving bytes, spent time, protocol version, host name, user agent, cookie, previous page of visitor and so on. Web log pretreatment reorganizes the web log into suit model for mining by a series of steps, the data pretreatment usually includes the following process: data cleaning, user identification, session identification, path addition.

This system uses Microsoft SQL Server as database, first, the web log with text format is loaded into database by data loading tool, and then the redundant files are deleted by inquiry analyzer, whose expansion name is gif or jpg or js or css or ico.

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The SQL sentences are as follows:. A shopping website is set up for test, all users are in the local network, each user has specific IP address without agent's disturbance. Therefore, different IP address represents different user and user identification is simplification. In order to get sequence page of the same IP address ordered by visit time, the visit time is set as the primary key and user's IP address is set as the second key.

The SQL sentences for this are as follows: Select from log group by cip order by time. The visitor cites buffer page in the session and connects requested page, the saved session in the session table is incomplete, it must be added path according to the website structure.

The sequence of user's visit can be obtained by adding path. Some visit path in the simulation test is shown as figure 1. For loading them into database and recognition, the symbol " " is used to partition item collection.

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The web log data which has been pretreated is very suitable for data mining, it is loaded on weka and mining algorithm may be selected, and related parameter can be set. For example, algorithm Apriori is selected, the mining parameter can be set which is shown as figure 2. After setting mining parameter, the data mining begins. The result of association rule mining is shown as figure 3.

In the actual homepage, It is discovered that this goods is the latest style notebook computer on market. According to the mining information, the businessman should increase the purchase quantity of this style notebook and advertise it on key position to raise sales. Therefore, from the first and the second association rule, the commentary to this style goods should be examined timely, and the more detailed parameter of goods should be provided to attract consumer to purchase.

In addition, the notebook quantity should be increased with the enhance of people's living standard. The third association rule shows that customers are very interested in the tendency of commercial city, specially the news of commercial city. Therefore, the tendency and news of commercial city should be renewed timely, and this page can be used to advertise the commercial city, and the customers would not think that the service of the commercial city is slow and would have a good impression to it.

The fourth association rule shows that customers enter enterprise's mailbox by feedback on message board, and the population is many. Therefore, in order to receive customer's feedback information timely and provide better service for customer, the mailbox link should be set up on the website homepage, it makes customer's complain convenient, and persuades old customer to stay and attracts new customers.

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This system uses the data mining platform weka, the classical mining algorithm Apriori is embed on weka platform, the function of association rule mining has been realized. Simulation test takes the visit log of a shopping website as data source, the characteristic attribute of user's behavior is collected, the visit characteristic of user is discovered, policy decision and service are provided for the website manager.

Mining Web navigation patterns with a path traversal graph[J] Expert Systems with Applications, ,38 6 : p. Data preprocessing evaluation for web log mining: reconstruction of activities of a web visitor[J]. Procedia Computer Science, ,1 1 : p. I never needed anyone to do my homework for me until I got a part-time job in college. Good thing I did my research and chose this website to outsource all the essays. I chose this site for affordable prices and excellent support.

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