Data mining method for quickly finding utility pattern

A model and utility technology, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of scalability and efficiency bottlenecks, scalability bottlenecks, and high storage space overhead.

Inactive Publication Date: 2012-09-12
ZHEJIANG GONGSHANG UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Not only does this cause excessive storage space overhead, leading to a scalability bottleneck for the first stage, but also for the second stage, and ultimately leads to inefficient runtime
[0006] In order to overcome the defects of previous mining methods, the pres

Method used

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  • Data mining method for quickly finding utility pattern
  • Data mining method for quickly finding utility pattern
  • Data mining method for quickly finding utility pattern

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Embodiment Construction

[0079] The present invention "a data mining method for quickly discovering utility patterns" proposes three innovative technologies. figure 1 Summarize the integration route of these three innovative technologies.

[0080] Below in conjunction with accompanying drawing and example (given figure 2 The transactional database D, image 3 The shown utility information table UT, utility threshold minutil=30) divides the technical solution into two processes for further description.

[0081] Process 1: Use a sparse matrix implemented by a linear linked list to merge and express the database D and the utility information table UT, that is, the transaction record set TS({}) supporting the empty pattern {} and the complete information of its utility (“invention Content A1").

[0082] The specific steps of process one are as follows:

[0083] 1.1 ScanDatabaseOnceforOmega: Execute "Content of the Invention Step A1.1".

[0084] Scan the database D for the first time and calculate t...

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Abstract

A data mining method for quickly finding a utility pattern can find a utility pattern which not only has substantial statistical characteristics but also meets user expectations and user goals from massive data, having a wide application in network information search and knowledge discovery. Aiming at solving the present problems of high time overhead and space overhead of existing methods caused by adoption of a two-stage method which generates a candidate pattern, the present invention provides three innovative technologies. The first is data representation based on a sparse matrix and virtual projection, the second is a prefix growth strategy, a prefix growth tree and a tailoring method thereof, and the third is a depth-first dynamic search method. With the three innovative technologies, a novel mining method is designed which has a single stage, causes no candidate pattern, and enables mining the utility pattern. The time efficiency ratio of the data mining method is higher by one to three orders of magnitude than that of other three referential mining methods, and the memory usage is reduced by 40% to 90%. The present data mining method has a high performance and enables various applications such as massive Web mining, multimedia mining and test mining.

Description

technical field [0001] The invention relates to the field of intelligent information processing. The present invention designs a utility mode mining method that can discover not only significant statistical features but also meet user expectations and goals from massive data, especially in massive data mining, especially network information search and knowledge discovery, including Web mining, text mining, multimedia In mining, it has broad application prospects. Background technique [0002] Traditional data mining technology, especially frequent pattern mining technology [1][2], mainly analyzes data based on statistical significance, such as digging out product combinations with high purchase frequency from supermarket sales data, without considering the user's Expectations or goals, such as the user may be interested in a product portfolio with a higher profit return. That is to say, in data mining, not only the statistical significance of the data, but also the user's ...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 刘君强蒋晓宁甘志刚余斌霄
Owner ZHEJIANG GONGSHANG UNIVERSITY
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