International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 6, June 2014) 1 Enhanced Fuzzy Association Rule Mining Techniques For Prediction Analysis in Betathalesemia's Patients Siji P.D 1 , Dr.M.L.Valarmathi 2 , 1. Assistant Professor, Department of Computer Science, St. Josephs College Irinjalakuda, Thrissur, 2 Associate Professor, Department of Computer Science & Information Technology, GCT Coimbatore-13, 1. srblessy@gmail.com 2. ml_valarmathi@rediffmail.com Abstract— This paper is a study into a fuzzy association rule mining model for better prediction performance in a particular medical database. The model (the FCM–MSMM- Apriori model) integrates multi membership and multiple support approach for Betathalasemia disease for performance prediction. The idea of multiple membership functions is measured to offer fuzzy quantitative information, selecting best one out of the different membership functions. Betathalasemia data which usually has a quantitative structure in nature. Traditional data mining algorithms such as association rules, when applied alone, often produce uncertain and unreliable results. The new algorithm focuses on characteristics of the variety structure of data, and the association rules of data causes can be calculated more accurately and in higher rates. The Novel application of Multi Support Multiple membership function to the attributes according to their nature gives better prediction and good performance analysis Keywords—Fuzzy association rules, Clustering, FCM- Apriori algorithms, Multiple Membership, Multiple Support INTRODUCTION Association rule mining has been a accepted area in data mining (DM) research, more and more attracting the attention of researchers.[1] [2][3][4][5] are important works in this area. Association rules discovery presented in [6] intends to extract the characteristics, hidden association patterns and the correlation between the items (attributes) in a large database [7],[8]. The Apriori algorithm developed by [9] is a classic and popular algorithm for strong association rules (knowledge) extraction from a transaction database with high frequent itemsets using the pre-defined threshold measures. These thresholds are minimum support (minsupp) and minimum confidence (minconf). Association rules are formally written and presented in the form of ‗‗IF–Then‘‘ as follows: X → Y, where X is called the antecedent and Y is called the consequence. Let I = {i 1 , i 2 , . . . , i n } be a set of distinct items (attributes). A collection of one or more items, i.e., any set of items is called an item-set. Let D = {t 1 , t 2 , . . . , t m } be a set of transaction IDs (TIDs). Each TID in D is formed from a set of items in I. The support count is the occurrence (frequency) of X and Y together, support (XUY), and the support value is the fraction of transactions that contains both X and Y. An item set whose support is greater than or equal to a minsupp threshold is called a frequent item set. The confidence value measures how often items in Y appear in transactions that contain X and is the ratio of occurrence (X and Y) divided by (/) occurrence(X) . Support(XUY)/Support(X) An association rule is an implication expression of the form (: X → Y), where X, Y I and X ∩Y =ϕ A strong association rule is that which has support and confidence greater than the user defined minsupp and minconf. The main task of the association rule discovery is to find all strong rules. One of the advantages of association rule discovery is that it extracts explicit rules that are of practical importance for the user/ human expert to understand the application domain. Therefore this can be facilitated to adjust (extend) the rules manually with further domain knowledge, which is difficult to achieve with other mining approaches [10] . On paper [11] introduced the problem of extracting association rules from quantitative attributes by using the partitions method for these attributes. Some of the current association rule mining approaches for quantitative data neglected the values of the interval boundaries of the partitions. This causes sharpness of the boundary intervals which does not reflect the nature of human perception, justifiably argued by [12] 13]. Instead of using partition methods for the attributes, it is better to adopt the advantage of fuzzy set theory with a smooth transition between fuzzy sets. As a whole, the fuzzy approach is used for transforming quantitative data into fuzzy data. A variety of approaches has been developed in order to extract fuzzy association rules from quantitative data sets [14],[15],[16],[17],[18],[19],[20],[21]. In this paper investigates the problem of association