Improved Apriori algorithm based method for mining database association rule

A database and rule technology, applied in database model, relational database, electronic digital data processing, etc., can solve problems such as large storage space

Active Publication Date: 2016-02-10
CHINA INFOMRAITON CONSULTING & DESIGNING INST CO LTD
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AI Technical Summary

Problems solved by technology

This algorithm overcomes the shortcomings of Apriori and its related algorithms that generate a lar

Method used

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  • Improved Apriori algorithm based method for mining database association rule
  • Improved Apriori algorithm based method for mining database association rule
  • Improved Apriori algorithm based method for mining database association rule

Examples

Experimental program
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Embodiment 1

[0077] through as figure 1 A simple transaction database D shown describes the steps of the OLA algorithm, and conducts a simple analysis of its performance, and sets the minimum support degree min_support=30%.

[0078] 1) According to the OLA algorithm, first of all figure 1 The transaction database D shown is scanned, and the transaction database D contains a total of 10 transactions T 1 -T 10 , 6 items I 1 -I 6 . Scanning the transactional database D will yield results such as figure 2 The relationship matrix A shown, the i-th row of the relationship matrix A corresponds to the transaction T of the database D i , i∈[1,10], the jth column corresponds to the item Ij in the database D, j∈[1,6], the non-zero element a in the relationship matrix ij Indicates item I j Included in transaction T i middle. Represent the relationship matrix A with an orthogonal linked list, such as image 3 as shown, image 3 The node of type M is the header node of the orthogonal linked...

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Abstract

The present invention proposes an improved Apriori algorithm based method for mining a database association rule. According to the method, a transaction database is converted into a relational matrix, the converted relational matrix is a sparse matrix, and the relational matrix is stored with an orthogonal link list. A generation process of a frequent item set is converted into an operation process of a single link list node set corresponding to items in the corresponding relational matrix. According to the method, a database only needs to be scanned once, so that the shortcomings that Apriori and a related algorithm therefor generate a large amount of candidate sets and need to scan the database for multiple times are overcome, and the time of frequently performing I/O operations is shortened; then, when a frequent 2-item set is generated and found, only an intersection operation of a node set needs to be performed, so that less time is consumed; and a single link list constructed by a generated frequent k-item set is recorded, so that a generation process of a frequent K+1-item set is simplified, and a complex pruning process of the Apriori algorithm is avoided.

Description

technical field [0001] The invention discloses a database association rule mining method based on the improved Apriori algorithm, which focuses on transforming and optimizing the frequent item set generation process of the Apriori algorithm on the basis of expressing the transaction database with an orthogonal linked list storage matrix, which belongs to computer data mining and information processing technology. Background technique [0002] Today, with the rapid development of big data technology, people gradually realize that data is wealth, especially the analysis of business data has great practical value. As one of the main methods of data mining, association rule analysis is an indispensable and important part of data mining technology. It is mainly used to discover valuable and interesting connections and rules hidden in large transaction databases. Therefore, the research on association rule algorithms is of great significance. [0003] As early as 1993, IBM's com...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/284
Inventor 赵学健袁源孙知信乔爱锋
Owner CHINA INFOMRAITON CONSULTING & DESIGNING INST CO LTD
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