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Self-adaptive Web site method based on improved chameleon algorithm

A self-adaptive, site technology, applied in computing, website content management, computer parts and other directions, can solve the problems of increasing the difficulty of use, artificial, artificial similarity function, etc., to achieve good adaptability and flexibility, simplifying the difficulty. ease of effect

Inactive Publication Date: 2019-05-21
CHINA UNIV OF MINING & TECH
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AI Technical Summary

Problems solved by technology

The Chameleon algorithm can find high-quality natural clusters of arbitrary shape, size and density and has the characteristics of fast and efficient one-pass clustering algorithm, but this algorithm has the following disadvantages: 1), the determination of the K value in the K nearest neighbor graph requires manual work Carrying out: 2), difficulty in selecting the minimum bisection: 3), the threshold of the similarity function needs to be given manually
These shortcomings increase the difficulty of use and affect the unsupervised nature of clustering

Method used

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  • Self-adaptive Web site method based on improved chameleon algorithm
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  • Self-adaptive Web site method based on improved chameleon algorithm

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Embodiment Construction

[0019] Combine below figure 1 A kind of adaptive Web site method based on the improved chameleon algorithm of the present invention

[0020] for further clarification.

[0021] Step 1, preprocessing the Web log files, clearing the Web applications sent by the search engine in the log files and loading the rest of the data into the database. Delete the image application in the log. If the time interval between two adjacent Web applications is greater than a certain threshold T, they are considered to belong to different session processes. As a rule of thumb, set the time threshold at half an hour.

[0022] Step 2. Set the size of the sliding window. In the same dialogue application process, the two pages in the sliding window can be expressed as (P i , P j ), the two pages are considered related. Count all the sessions, and calculate the number of applications N for any two teams of pages ij . It is also necessary to calculate the number of separate applications N for e...

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Abstract

The invention discloses a self-adaptive Web site method based on an improved chameleon algorithm, and relates to the fields of cluster analysis, machine learning and artificial intelligence. The invention aims to overcome the defects in the prior art, and provides a method for applying an improved CHAMELEON algorithm to self-adaptive Web site construction. The algorithm comprises the following steps: preprocessing a Web log; establishing a distance matrix between pages according to the number of times that applications among different Web pages within the size of a sliding window are sent outat the same time, applying the distance matrix to an improved CHAMELEON algorithm, generating an initial weighted graph, and conducting sorting, cluster merging and repeating through the size of structural equivalent similarity until the modularity is reduced and terminated; and finally generating an index interface according to a cluster result.

Description

technical field [0001] The invention discloses an adaptive Web site method based on an improved chameleon algorithm, and relates to the fields of cluster analysis, machine learning and artificial intelligence. Background technique [0002] At present, with the development of information technology, various websites are becoming more and more complex, but it is not intelligent. The behavior patterns of users browsing websites are dynamic and diverse, and most websites are designed as rigid HTML or some sites that only change certain information dynamically. How to attract more users to visit the website and provide users with the information he is interested in has become the primary issue in the design of each website. Therefore, various Web sites compete to use the self-adaptive site construction technology based on Web mining. The Chameleon algorithm can find high-quality natural clusters of arbitrary shape, size and density and has the characteristics of fast and effici...

Claims

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Application Information

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IPC IPC(8): G06F16/958G06K9/62
Inventor 丁世飞王冬晨
Owner CHINA UNIV OF MINING & TECH
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