Method for quickly applying negative sequence mining patterns to customer purchasing behavior analysis

Abstract

The invention provides a method for quickly applying negative sequence mining patterns to customer purchasing behavior analysis. A fast algorithm named as f-NSP is proposed to efficiently mine the negative sequential patterns. According to the main idea of the algorithm, firstly, a positive sequential pattern is obtained through a positive sequential pattern mining algorithm, and as for all frequent positive sequences, an efficient bitmap storage structure is used for storing data sequences including the frequent positive sequences; secondly, negative candidates are generated through a method which is the same as e-NSP and is used for generating negative candidate sequences; finally, data bitmaps are subjected to and operation, or operation and xor operation by means of a formula, the support degree of the negative candidates is quickly calculated, the negative sequential pattern meeting the minimum support degree is mined, and a database does not need to be scanned again. Purchasing behaviors of customers are analyzed through screened sequential patterns so that a seller can predict subsequent commodity buying and selling conditions according to current commodity buying and selling conditions; as a result, the seller can arrange placement of commodities better, and the sale quantity of the commodities can be increased.

Description

technical field [0001] The invention relates to the application of a fast negative sequence mining pattern in the analysis of customer purchase behavior, and belongs to the application technical field of the negative sequence pattern. Background technique [0002] With the advent of the Internet upsurge, the number of online shopping users continues to increase. For consumers, online shopping has become a brand new shopping experience and has gradually become an indispensable part of life. The Internet provides a new interactive shopping channel, and consumers get huge advantages: rich commodity information, overcoming geographical and time barriers, obtaining competitively priced commodities, personalization and customization of products, and more commodities selection, greater shopping convenience and more. In recent years, online shopping has grown explosively, with geometric growth every year. At the same time, many large-scale e-commerce websites, such as Amazon, Alib...

AI Technical Summary

Problems solved by technology

However, most of the current negative sequence mining algorithms are very inefficient, and there are many difficulties in mining negative patterns, because negative patterns do not conform to the Apriori rule, so traditional pruning methods cannot be used to reduce the generation of negative candidate sequences, so large The negative candidate sequences of some algorithms are very large, such as PNSP and Neg-GSP

And when calculating the support of negative candidate sequences, it is often necessary to scan the database repeatedly, which brings great time and space consumption, making it more difficult to mine negative sequence patterns, so we need an efficient and fast negative sequence mining algorithm to solve the current problem

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