Mining of Removable Closed Patterns in Goods Dataset

Abstract

Production factories have many items produced with various parts to bring out the complete product at the end. Every product that is produced in the factory obtains the company some amount of income. The manufacturing of these products also costs a huge sum for purchase and production. When a situation arises for the company to shut down its manufacturing or when there is some crisis, the industry will not have money to buy the parts to finish the product. The problem of removable pattern mining is to identify the patterns which can be removed to reduce the loss to the factory’s profit under certain conditions. In such situations, the production managers need an alternate to reduce the cost of production without decreasing the profit rate. This paper proposes removable closed patterns to represent and compress the mined removable patterns without much profit reduction. Erasable closed pattern mining algorithm is implemented to mine all the erasable closed patterns by reducing the resources needed for production without affecting the information loss. The removable itemset with one pattern along with their element occurrence pair is taken into consideration. Further the erasable patterns are mined and used for useful extraction of members. Then the erasable closed patterns are mined using the module that checks the closeness of the patterns in the product datasets.