A Bitmap Approach for Mining Erasable Itemsets

Abstract

Erasable-itemset mining is a valuable method of pattern extraction for helping the manager of a factory analyze production planning. The erasable itemsets derived can be considered important production information regarding how to plan the production of a factory during an economic depression or financial shortage for the manager. After the erasable-itemset mining was proposed in 2009, several efficient mining approaches for finding erasable itemsets have been developed. However, these methods require a considerable amount of execution time when the amount of product data is large. Especially, manufacturing small amounts of versatile products has been a trend today, and it will generate a large product database. This paper adopts a bitmap representation for itemsets in an erasable-itemset mining algorithm to speed up the execution. Unlike the traditional bitmap meaning for frequent itemsets, a bitmap for erasable-itemset mining here denotes the relationship that a product includes at least one material (item) in a specified itemset. Using the bitmap representation can easily find the desired products to check, thus decreasing the scans of a database. Experimental evaluation on synthesized and real datasets was used to compare the proposed approach with the other two under different parameter values. The experimental results show that the proposed approach can make a good trade-off between execution time and memory usage.