The Research And Implementation Of Quantitative Association Rules

In the era of information explosion, faced to the challenges that"people were drowned data, while still feel knowledge hunger", data mining techniques have emerged and flourish. Data mining of association rules has become an important research area. The current research are mostly based on the support-confidence theory of boolean data mining, and made some research achievements, but the existed methods of boolean association rules from these data mining potential rules are insufficient. The critical of quantitative association rules problem to boolean association rules problem changing is how to divide the sections. The critical part of quantitative association rule mining is to partition the domains of quantitative attributes into intervals.The existed methods in divide the sections dealt with this problem by dividing the domains of quantitative attributes into equi-depth or equi-width intervals, or using a clustering algorithm on a single attribute (or a set of attributes) alone. Although these algorithms can be satisfactorily resolved quantitative data mining, but can not avoid the conflict between the minimum support and the minimum confidence problem, and risk missing some important rules. In this paper, the proposed method is the fact that a transaction as a n-dimensional vector and apply a iterative self-organizing data techniques algorithm(ISODATA) to all attributes clustered. Because explore of ISODATA, and can be combined human-computer Interaction and used the intermediate results of the experience gained to classify better. Clustering algorithm to the vectors, then project the clusters into the domains of the quantitative attributes to form overlapped intervals. Finally use a classical boolean algorithm to find association rules. This approach takes the relations and the distances among attributes into account, and can resolve the conflict between the minimum support problem and the minimum confidence problem by allowing intervals to be overlapped. Experimental results show that this approach can efficiently find quantitative association rules, and can find important association rules which may be missed by the previous algorithm.