Computer Science and Electrical Engineering Session:

Title:            An Innovative Progress Approach for Quantity Mining of Itemsets
Authors:       Prathyusha Kanakam, A.S.N. Chakravarthy
Abstract:      In recent years, Data mining has a great deal of attention not only in the information industry and but also in society. Due to wide availability of huge amounts of data and imminent need for turning such data into useful information and knowledge, this information can be used for various applications ranging from market analysis, fraud detection and customer retention, to production control and science exploration. Frequent Itemset Mining is the one of the important tasks of data mining, which is used to find the correlations between items stored in database. Many algorithms have been developed and implemented so far to produce frequent itemsets and considers only the presence and absence of an item in a transaction. In this paper, we introduce a new concept called Quantity Mining has recently been an emerging topic in the field of data mining. It finds out high-utility (on-demand) itemsets by considering both the profits and quantities of items in transactions and proposed an approach to not only generate frequent itemsets and but also their quantities in an easier and simpler way which will help in production control.

Keywords:    Frequent Pattern Mining; Data Mining; Quantity Mining
Pages:          494-498

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