International Journal of Trend in Scientific Research and Development, Volume 1(4), ISSN: 2456-6470 www.ijtsrd.com 179 IJTSRD | May-Jun 2017 Available Online @www.ijtsrd.com Data Mining For Supermarket Sale Analysis Using Association Rule Mrs. R. R. Shelke H.V.P.M. COET, Amravati Dr. R. V. Dharaskar Former Director DES (Disha – DIMAT) Group of Institutes, Raipur Dr. V. M. Thakare Prof. and Head, Computer Science Dept., SGB Amravati University, Amravati ABSTRACT Data mining is the novel technology of discovering the important information from the data repository which is widely used in almost all fields Recently, mining of databases is very essential because of growing amount of data due to its wide applicability in retail industries in improving marketing strategies. Analysis of past transaction data can provide very valuable information on customer behavior and business decisions. The amount of data stored grows twice as fast as the speed of the fastest processor available to analyze it. Its main purpose is to find the association relationship among the large number of database items. It is used to describe the patterns of customers' purchase in the supermarket. This is presented in this paper. KEYWORDS: Data mining, Associations, supermarket I. INTRODUCTION Data mining tasks can be classified into two categories: Descriptive mining and Predictive mining. Descriptive mining refers to the method in which the essential characteristics of the data in the database are described. Clustering, Association and Sequential mining are the main tasks involved in the descriptive mining techniques tasks. Predictive mining deduces patterns from the data in a similar manner as predictions. Predictive mining techniques include tasks like Classification, Regression and Deviation detection. Mining Frequent Itemsets from transaction databases is a fundamental task for several forms of knowledge discovery such as association rules, sequential patterns, and classification. An itemset is frequent if the subsets in a collection of sets of items occur frequently. Frequent itemsets is generally adopted to generate association rules. The objective of Frequent Item set Mining is the identification of items that co-occur above a user given value of frequency, in the transaction database. Association rule mining is one of the principal problems treated in KDD and can be defined as extracting the interesting correlation and relation among huge amount of transactions. II. LITERATURE REVIEW Association Rule Discovery has become a core topic in Data Mining. It attracts more attention because of its wide applicability. Association rule mining is normally performed in generation of frequent itemsets and rule generation in which many researchers presented several efficient algorithms [1-5]. T. Karthikeyan and N. Ravikumar, aim at giving a theoretical survey on some of the existing algorithms [3]. The concepts behind association rules are provided at the beginning followed by an overview to some of the previous research works done on this area. The advantages and limitations are discussed and concluded with an inference. Association rule