Study On Data Mining Of A Temporal Association Rules

With the frequent interaction of business and the elevation of corporation's information degree, more and more information are being accumulated, most of which exist as a form of temporal data. Therefore, temporal data mining (TDM) becomes a very interesting field of data mining. The study of temporal data mining is at its early stage, till now the effect of the arithmetic for temporal data mining is not idea. There exist many problems that need to be studied, therefore we discuss TDM in this paper.First, we discuss the background and development of TDM, and describe the researching content in this paper.Second, we introduce the basic concepts about temporal type, temporal factor and temporal granularity, propose a kind of mathematical model about temporal association rules, and describe several temporal association rules with practice significance.Third, we discuss the temporal association rules mining algorithm of single event during the period of same temporal factor, cycle temporal factor and analyze the experiment results. To mine temporal association rules in two events, we propose a new increment algorithm. Moreover, their experiment results are given. We propose a new association rules mining algorithm based interest measurement, and give the experiment results.Finally, we discuss applications of Markov Chain in TDM.In this paper, we obtain these results: 1 a kind of mathematics model of temporal association rules; 2 propose an algorithm of single event during the period of same temporal factor, an algorithm of cycle temporal factor, an increment algorithm about temporal association rules mining of two events and an algorithm based interest measurement.