Temporal association rules discovery algorithm based on improved index tree

Chen Yuanyuan,Wang Rui,Zeng Bin,W. S. Griffith

doi:10.2478/amns.2021.1.00016

Chen Yuanyuan, Wang Rui + Show 2 more

Open Access

https://doi.org/10.2478/amns.2021.1.00016

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Abstract

Abstract With the rapid increase of information generated from all kinds of sources, temporal big data mining in business area has been paid more and more attention recently. A novel data mining algorithm for mining temporal association is proposed. Mining temporal association can not only provide better predictability for customer behaviour but also help organisations with better strategies and marketing decisions. To compare the proposed algorithm, two methods to mine temporal association are presented. One is improved based on a traditional mining algorithm, Apriori. The other is based on an Index-Tree. Moreover, the proposed method is extended to mine temporal association in multi-dimensional space. The experimental results show that the Index-Tree method outperforms the Apriori-modified method in all cases.

Highlights

Data mining is motivated by the decision support problem faced by many organisations
The first algorithm we propose to solve the problem of mining frequent intervals is revised from the Apriori algorithm
The Index-Tree is an extended prefix-tree structure which is capable of storing crucial, quantitative information about the intervals

Summary

Introduction

Data mining is motivated by the decision support problem faced by many organisations. Most of the proposed methods developed in mining frequent patterns focus on the problems with discrete data items such as customers’ buying data in a supermarket. There exist some problems where the data items are associated with intervals (continuous or discrete) in database transactions [1, 2]. Time intervals in which customers are more likely to use their cellular phones, the cellular phone companies can be more comprehensive of the usage patterns of customers and can make better marketing decisions or provide better services. The new mining frequent intervals can provide better predictability for customer behaviour and help organisations with better strategies and marketing decisions

Problem description

Apriori modified method

Experiment results

Conclusions