Hybrid System to find Association using Genetic Algorithm and Fuzzy Logic

Now a day, so many technologies are evolving around to find solution of the problem. Association rule mining is a well-known technology used to find the association among the different attributes in any field. ARM cannot work directly on numerical values, so concept of fuzzy logic will applied to assign Linguistic variable. And Genetic algorithm is an evolutionary algorithm to get the optimized result. GA is based on the theory of survival. Fittest chromosome has high probability of surviving. In this paper, author used the concept of genetic algorithm in association rule mining to get the frequent itemset. And before applying genetic algorithm, data has to be fuzzify to support ARM algorithm. With the help of this implementation rare and popular, both cases will be considered to derive the associations among the parameters which can be used further in any decision making strategy. I.INTRODUCTION Data Mining in itself is a package with many algorithms well suited to solve complex problems.Data mining algorithms can be implemented for prediction, classification, pattern detection, finding association, series analysis, etc on different types of data such as temporal, spatial, geospatial, day to day data and many more.Now a day, data is exploded in huge amount in every application and data mining algorithms has broad area of application with variety of data.Though in Data Mining, Association rule extraction is the most widely based exploration technology and mainly used to find hidden relationships between data in order to generate classification clusters, wherein data items are combined based on their various granularity levels.The most popular example of association rule extraction is market basket analysis.Data Mining techniques are used to make decisions in business strategy, given that traditional methods of Big data analysis have become inefficient and show poor performance.Association rule mining [1] can be used in many areas to predict the association among the influencing factor of that application.In medical science, we can predict the association of different parameters with its values for the chance of having any disease.This will help to control the factors so that the disease will not occur.The remaining of this paper is organized as: section 2 gives the introduction to the basic concept of association rule mining, Genetic algorithm and Fuzzy set theory, section 3 gives the related work in the same field, section 4 gives the hybrid system used in association rule mining using genetic algorithm and fuzzy set theory for the chance of having heart disease, section 5 gives the experimentation results of the implemented system, finally, the system will be concluded along with future work in section 6. II. BASIC CONCEPTS A. Association Rule Mining (ARM)Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases.It is intended to identify strong rules discovered in databases using some measures of interestingness.Initially Market basket analysis [1] is the major application of ARM.Based on the concept of strong rules, association rules can be formulated for discovering regularities between products in transactional data recorded by systems in supermarkets.For example, the rule {onion, potato}->{burger bun} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy burger buns.Such information can be used as the basis for decisions making about marketing activities such as, e.g., promotional pricing or product placements.Now a day, ARM can be used in many application areas including Web usage mining, intrusion detection, continuous production, bioinformatics and many more.Features used in ARM are: Support: Support is an indication of how frequently the itemset appears in the dataset.The support of X with respect to T is defined as the proportion of transactions t in the dataset which contains the itemset X.