Transactional Utility Mining for Enterprise Using Selective Item Replication

ABSTRACT Our method operates on a graph where vertices correspond to frequent items and edges correspond to frequent item sets of size two. This distribution entails an amount of data replication, which may be reduced by setting appropriate weights to vertices. The data distribution scheme is used in the design of two new parallel frequent item set mining algorithms. Both algorithms replicate the items that correspond to the separator. Utility based data mining is a new research area interested in all types of utility factors in data mining processes and targeted at incorporating utility considerations in data mining tasks. The UMining algorithm is used to find all high utility item sets within the given utility constraint threshold. Another algorithm, Fast Utility Frequent Mining, is a more precise and very recent algorithm. It takes both the utility and the support measure into consideration. This method gives the item sets that are both high utility as well as that are, frequent. A proposed for generating different kinds of item sets NoClique are High utility and high frequent item sets (HUHF), High utility and low frequent item sets (HULF), NoClique2 are Low utility and high frequent item sets (LUHF) and Low utility and low frequent item sets (LULF). These item sets are generated using the basic framework of FUM and FUFM algorithms. Customer Relationship Management (CRM) is incorporated into the system.
CHAPTER 1

INTRODUCTION

1.1 LITERATURE SURVEY

1.1.1 Association Rule Mining The sets of items (for short item sets) X and Y are called antecedent (left-hand-side or LHS) and consequent (right-hand-side or RHS) of the rule In data mining association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. Piatetsky-Shapiro describes analyzing and presenting strong rules discovered in databases using different measures of