IJSRST1845464 | Received : 05 April 2018 | Accepted : 20 April 2018 | March-April-2018 [ (4) 5 : 1678-1684] © 2018 IJSRST | Volume 4 | Issue 5 | Print ISSN: 2395-6011 | Online ISSN: 2395-602X Themed Section: Science and Technology 1678 Comparative Analysis of On-Shelf Utility Mining Algorithm Dr. S. Vijayarani 1 , Mrs. C. Sivamathi 2 ,Ms. V. Jeevika Tharini 3 1 Assistant Professor, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India 2 Ph. D Research Scholar, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India 3 PG student, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India ABSTRACT Data mining is a process of retrieving previously unknown and needed patterns from database. Utility mining is one of the important fields in data mining. Utility mining is a process of finding high utility itemsets from a database. An item is termed as high utility item if the item’s utility is more than minimum threshold value. Utility of an item is based on user’s interest or preference. Recently, temporal data mining has become a core data processing technique to deal with the changing data. On-shelf utility mining includes the on-shelf time period of item and gets the exact utility values of itemsets in temporal database. In traditional on-shelf utility mining, profits of all items in databases are considered as positive values. However, in real applications, some items may have negative profit. In this work both FOSHU (Faster On-Shelf High Utility) and TS-HOUN (Three-Scan Algorithm for Mining On-shelf High Utility Itemsets with Negative profit) algorithms are compared and their performances were measured. Keywords : Utility mining, On-Shelf utility mining, temporal database, relative utility, periodical utility. I. INTRODUCTION Data mining is the process of extracting interesting information or patterns from large information repositories. It task includes finding association rules, classification rules, clustering rules. Among those data mining, association rule mining is the most popular task in data mining. It has two phases. In first phase, it discovers all the frequent itemsets based on a user- defined minimum support threshold value. In second phase, it generates the association rules from the discovered frequent itemsets based on the user- defined minimum confidence threshold value. In this, frequent itemsets considers only the frequency of an item in a database. The relative importance such as price, weight or profit of an item inside a transaction is not considered. However, in real world business, some items or itemsets with low support in the data set may bring high profits due to their high price or high frequency within transactions. Such useful, profitable itemsets are missed by frequent itemset mining [1]. In Weighted Frequent itemset mining, weights of each item such as unit profits of items in the databases are considered. If items appear infrequently, they might still be found if they have high weights. But in this framework, the quantities of items are ignored. Therefore it cannot satisfy the requirements of users who are interested in finding the itemsets with consideration of both quantity and profit [2]. Recently, Utility itemset mining [3] has been proposed to eliminate the limitation of frequent and weighted itemset mining. It considers utility of an item which is based on interesting measures like user’s preference or frequent patterns of interest. Utility mining measures the importance of an item. Thus it is useful in real world market data analysis. Utility of an item in a database is the product of external and local transaction utility values. The local transaction utility is defined as the quantity of a item and the external utility is the profit of a item in utility mining. The utility of an itemset is calculated by the product of quantity and profit. If utility of an itemset is greater than the threshold (predefined (user defined)