Abstract

The paper presents an algorithm CRMS of classification rule mining based on multi-support, in which the frequent classification item-set tree is adopted to organize the frequent pattern sets, and array to figure the classification projected transaction subsets. The multi-support method is applied in CRMS due to the unevenly distributed classification patterns. The CRMS uses the breath first strategy assisted by the depth first strategy, and adopts pseudo projection, which makes it unnecessary to scan the database and construct the projected transaction subsets repeatedly, therefore the memory and time cost is very low, and the projecting-efficiency and scalability are higher. The CRMS algorithm can be used in the basket analysis, association rule mining for consumption behavior in the retailing industry, which supports the product layout, buying recommendation and the sale promotion.

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