The Research And Application Of Association Rules In Data Mining

Nowadays,the society has entered the information era. The computer and network information technology have been developed so rapidly that data and information are increasing dramatically (information explosion) in all fields,meanwhile data and information systems become more uncertain due to human's participation. How to effectively achieve the data analysis and processing and quickly get implicit knowledge has long been an important direction of artificial intelligence. In this background,Knowledge Discovery in Databases(KDD) and Data Mining(DM) provide a new intelligent way of understanding data.In the prosperous background of data mining technology , association rules(ARs) technology obtains the vigorous development. Mining association rules aims at finding interesting correlations and associations from big volumes of data. Its application scope expands from the narrow-sense market basket analysis to the design and optimization of website,the network intrusion detection,the traffic accident pattern analysis,the analysis of medicine ingredient,the protein structure analysis, software bug mining ,and fault diagnosis for machine and so on. Its fundamental research contents also expend from the original frequent pattern mining to the close pattern mining,maximal pattern mining,extension association rule mining,privacy protection in ARs,incremental mining for ARs , post-mining process , subjective interesting measures,correlated patterns,and Ars mining from data streams et al. Therefore,it is necessary to have an in-depth study for related technologies of association rules.The key works in this thesis are as follows:1. Based on Rough Set Theory and Concept Lattice,a new method is proposed to search for association rules. The formal context is first reduced via Rough Set Theory. And then some of the attributes are thrown off for the given threshold. The complexity of constructing lattice and searching for the desire concept is deceased. The supports and confidences for the obtained association rules are computed with the aid of concept lattice.2. On the occasion of dealing with time series hailed from complex system, the investigation of series'local patterns and local relation- ship has distinct superiority over traditional global models. In order to find rules relating patterns in a time series to other patterns in that series, or patterns in one series to patterns in another series, a fuzzy sub-series discretization method, which soften the effect of sharp boundaries of delegate of each local sub-series, is proposed. Then the parallel algorithm for mining Boolean association rules is improved to discover frequent fuzzy attributes. Finally, the fuzzy association rules with least fuzzy confidence are generated by all processors.At last, the application prospect of association rule mining and further research.