IJSRST184139 | Received : 07 Jan 2018 | Accepted : 21 Jan 2018 | January-February-2018 [ (4) 2: 283-289] © 2018 IJSRST | Volume 4 | Issue 2 | Print ISSN: 2395-6011 | Online ISSN: 2395-602X Themed Section: Science and Technology 283 A Study and Analysis of Association Rule Mining Algorithms In Data Mining N. Yuvamathi * 1 , R. Porkodi 2 * 1 PG Scholar, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India 2 Assistant Professor, Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India ABSTRACT The data mining is a technology that has been developed rapidly. It is based on complex algorithms that allow for the segmentation of data to identify pattern and trends, detect anomalies, and predict the probability of various situational outcomes. The raw data being mined may come in both analog and digital formats depending on the data sources. There are many trends that are available in data mining some of the new trends are Distributed Data Mining (DDM), Multimedia Data Mining, Spatial and Geographic Data Mining, Time Series and Sequence Data Mining, Time Series and Sequence Data Mining . This paper is based on Association rule mining. In the field of association rule mining, many algorithms exist for exploring the relationships among the items in the database. These algorithms are very much different from one another and take different amount of time to execute on the same sets of data. In this paper, a sample dataset has been taken and various association rule mining algorithms namely Apriori, FP-Growth, Tertius have been compared. The algorithms of association rule mining are discussed and analyzed deeply. The main objective of this paper is to present a review on the basic concepts of ARM technique and its algorithms. Keywords : Data Mining, Association Rule, Apriori, FP-Growth, Tertius. I. INTRODUCTION Data mining concept was raised at the ACM conference in the United States in 1995.It is the computing process of discovering pattern in large data sets involving methods at the intersection of machine learning, statistics and database systems. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Data mining is the analysis step of the “Knowledge Discover of Database” process, or KDD. It is also called as KDD process. Data Mining plays an important role in many areas. Some of the important areas are Future Healthcare, Market Basket Analysis, Education, Manufacturing Engineering, Customer Relationship Management (CRM), Fraud Detection, Intrusion Detection, customer Segmentation, Lie Detection, Financial Banking, Bio informatics, Research Analysis, etc [1]. Recently, there are several data mining techniques that have been developed and used in data mining projects including classification, clustering, association rule, prediction, sequential patterns and decision tree. The Association rule is one of the best known data mining technique. Association rules are if/then statements that help uncover relationships between apparently unrelated data in relational database or other information repository. It has two parts, an antecedent (if) and a consequent (then). An antecedent is an item that found in the data. A consequent is an item that is found in combination with the antecedent [2]. Association rules are created by analyzing data for frequent patterns and using the criteria support and confidence to identify the most important relationships. Association rules use