Abstract. Mining erasable itemsets first introduced in 2009 is one of new emerging data mining tasks. In this paper, we present a new data representation called PID_list, which keeps track of the id_nums (identification number) of products that include an itemset. Based on PID_list, we propose a

A factory has a production plan to produce products which are created from number of components and thus create profit. During financial crisis, the factory cannot afford to purchase all the necessary items as usual. Mining of erasable itemsets finds the itemsets which can be eliminated and do

Abstract—This paper proposes an improved version of the MERIT algorithm, dMERIT+, for mining all “erasable itemsets”. We first establish an algorithm MERIT+, a revised version of MERIT, which is then used as the foundation for dMERIT+. The proposed algorithm uses: a weight index, a hash table

-turn will help in production planning of any company. This gain value is needed to prune some of the irrelevant items from the high utility patterns at the time of economic crisis through erasable itemset mining. But all of the existing erasable itemset mining algorithms are based on centralized database

Abstract — Erasable Itemset Mining is the key approach of data mining in production planning. The erasable itemset mining is the process of finding erasable itemsets that satisfy the constraint i.e. user defined threshold. Efficient algorithm to mine erasable itemsets is extremely important in data