Method for mining frequent weighting item set based on effective weighting tree

Abstract

The invention discloses a method for mining a frequent item set based on an effective weighted tree, comprising the following steps: S1, reading a database D from a network, the database D comprisingN transactions, each transaction comprising different items, the number of items and the weight w occupied by each item; S2, calculating a transaction weight tw of each transaction in the database D,and generating a transaction weight table; S3, calculating the weight W of the item set S, and presetting a threshold value minws, wherein if W is greater than or equal to minws, sthe item set S is afrequent item set; if W < minws, the item set S is a non-frequent item set; and S4, constructing an effective weighted tree model for mining the frequent item set. According to the method, weights aregiven to the item sets, weight calculation is carried out, so that an effective weighted tree model is constructed, the mining efficiency of the frequent item sets is improved, memory application isreduced, and the method is suitable for a database of big data.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method for mining frequent weighted itemsets based on an effective weighted tree. Background technique [0002] With the rapid development of computer technology, human beings have entered the era of big data. The processing, analysis and use of data has become increasingly important, and data analysis and data mining have gradually become the leading research fields of the times. Data mining is a key component of the field of knowledge discovery. As the name suggests, data mining is to obtain knowledge from big data. [0003] Association rule mining (ARM, Association rule mining) plays an important role in the field of data mining. ARM is used to identify the relationship between items in transactional databases, and frequent itemset mining plays an important role in it. Mining of frequent itemsets is one of the key technologies of data mining. [0004] In classic...

AI Technical Summary

Problems solved by technology

In 2009, B le, H Nguyen and B Vo proposed the WIT-tree method to mine frequent weighted itemsets, but its transactions per frequent itemsets, so the memory usage of the algorithm is high

In 2013, B Vo, F Coenen and B Le proposed the WIT-diff algorithm, using a difference set strategy to reduce memory usage and speed up the process, but in databases with a large number of items, it will run due to memory overflow Fails to get frequent weighted itemsets

Images