ISSN(Online): 2319-8753 ISSN (Print): 2347-6710 International Journal of Innovative Research in Science, Engineering and Technology (An ISO 3297: 2007 Certified Organization) Website: www.ijirset.com Vol. 6, Issue 3, March 2017 Copyright to IJIRSET DOI:10.15680/IJIRSET.2017.0603198 4170 A Technical Analysis of Market Basket by using Association Rule Mining and Apriori Algorithm M.Phil Research Scholar, Department of Computer Science, RVS College of Arts & Science, Sulur, Tamilnadu, India Associate Professor, Department of Computer Application, RVS College of Arts &Science, Sulur, Tamilnadu, India ABSTRACT: Data mining refers to extracting knowledge from large amount of data. Market basket analysis is a data mining technique to discover associations between datasets. Association rule mining identifies relationship between a large set of data items. When large quantity of data is constantly obtained and stored in databases, several industries is becoming concerned in mining association rules from their databases. For example, the detection of interesting association relationships between large quantities of business transaction data can help in catalog design, cross-marketing and various businesses decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased by customers. It is helpful to examine the customer purchasing behavior and assists in increasing the sales. This work acts as a broad area for the researchers to develop a better data mining algorithm. This paper presents a survey about the existing data mining algorithm for market basket analysis. KEYWORDS: Association Rule Mining, Apriori Algorithm, Market Basket Analysis. I.INTRODUCTION Association rule mining (ARM) is used for identification of association between a large set of data items. Due to large quantity of data stored in databases, several industries are becoming concerned in mining association rules from their databases. For example, The detection of interesting association relationships between large quantities of business transaction data can assist in catalog design, cross-marketing, and various business decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can help retailers to expand marketing strategies by gaining insight into which items are frequently purchased jointly by customers. This work acts as a broad area for the researchers to develop a better data mining algorithm. This paper presents a survey about the existing data mining algorithm for market basket analysis. This review paper is organized as follows: Section I contains brief introduction of ARM, Section II depicts market basket analysis which is an application of ARM, Section III discusses the literature survey in which various data mining algorithms are discussed, section IV discusses apriori algorithm, problems and directions of data mining algorithms are S.Pradeepkumar*, Mrs.C.Grace Padma**