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A Weighted Frequent Pattern Mining Method Based on Sliding Window

A sliding window and frequent pattern technology, applied in the field of communication, can solve the problem of less data flow weighted frequent pattern mining, and achieve the effects of high stability of space-time requirements, reduced space cost, and less system resource occupation

Active Publication Date: 2011-12-21
王洁 +1
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  • Summary
  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] At present, although some relevant research has been carried out on the problem of weighted frequent pattern mining, there are relatively few researches on the weighted frequent pattern mining of data streams for specific application problems.

Method used

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  • A Weighted Frequent Pattern Mining Method Based on Sliding Window
  • A Weighted Frequent Pattern Mining Method Based on Sliding Window
  • A Weighted Frequent Pattern Mining Method Based on Sliding Window

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Embodiment Construction

[0034] Such as figure 1 As shown, the present invention provides a data stream weighted frequent pattern mining method, which operates based on a sliding window, and the sliding window is a fixed number of transaction sequences on the data stream, and continues to slide forward as time progresses; The sliding window SW is divided into n sub-windows of the same size, that is, the basic window BW. We use the basic window as the update processing unit. The improvement is that the SWFP unit is used to dynamically maintain the effective data information of the data in the data stream sliding window, and realize weighted For data mining of frequent patterns, the SWFP unit includes a SWFP-Tree pattern tree and an item header table IHT; the above-mentioned mining method includes the following steps:

[0035] 1. Initialize the header table and SWFP-Tree;

[0036] 2. Add the newly arrived basic window to the sliding window;

[0037]3. Determine whether the sliding window is full. If t...

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Abstract

The invention provides a method for mining a data flow weighted frequent mode based on a sliding window. The method comprises the following steps: the weighted frequent mode stored in a data flow sliding window is compressed by an SWFP (Sliding Window Frequent Parting) unit; when a data flow passes, the method is only used for scanning data for one time; mode information and weight information incremental amount included in the data are updated in an SWFP-Tree mode tree in the SWFP unit; along with new data continuously flowing to the sliding window, expired data is deleted; and mode tree species non-frequency modes are deleted in a timed manner by using two pruning strategies so that the storage space of the SWFP-Tree mode tree is compressed. The method for mining the data flow weighted frequent mode provided by the invention has the characteristics of rapid speed, effectiveness and high time and space requirement stability and is applied to mining the weighted frequency mode in a data flow environment.

Description

technical field [0001] The invention relates to the field of communication, in particular to a data flow weighted frequent pattern mining method. Background technique [0002] Data mining technology has been widely used in Web log analysis, network security monitoring and so on. Data mining technology can effectively and accurately expand the user's access and retrieval to improve user efficiency; it can also mine the user's points of interest according to the actual user's browsing situation, regularly push relevant information for the user, and adjust the website The link structure and content of the web pages in the website provide users with customized services. [0003] Frequent pattern mining is the basis for discovering association rules. Traditional frequent pattern mining methods consider each item in the data to be equally important. However, in practical applications, certain items or patterns may be more important than others due to different user interest in t...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 王洁曾宇
Owner 王洁
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