Abstract
Association rules are used to discover all the interesting relationship in a potentially large database. Association rule mining is used to discover a small set of rules over the database to form more accurate evaluation. They capture all possible rules that explain the presence of some attributes in relation to the presence of other attributes. This review paper aims to study and observe a flexible way, of, mining frequent patterns by extending the idea of the Associative Classification method. For better performance, the Neural Network Association Classification system is also analyzed here to be one of the approaches for building accurate and efficient classifiers. In this review paper, the Neural Network Association Classification system is studied and compared in order to find best possible accurate results. Association rule mining and classification rule mining can be integrated to form a framework called as Associative Classification and these rules are referred as Class Association Rules. This review research paper also analyzes how data mining techniques are used for predicting different types of diseases. This paper reviewed the research papers which mainly concentrated on predicting Diabetes.
Highlights
Data mining refers to the process of extracting knowledge from large amounts of data
The association rule mining and classification rule mining can be integrated to form a framework called as Associative Classification and these rules are referred as Class Association Rules
Data mining would be a valuable asset for a diabetes researcher because it can unearthen hidden knowledge from a huge amount diabetes related data
Summary
Data mining refers to the process of extracting knowledge from large amounts of data. Association rule mining falls under the descriptive category. Association rules aims in extracting important correlation among the data items in the databases. Association rule mining is a descriptive data mining task. Association rules find interesting associations and/or relationships among large set of data items. They capture all possible rules that explain the presence of some attributes in relation to the presence of other attributes. It is important to analyze the relationships among the risk factors and Apriori algorithm is used for this purpose It is an powerful algorithm for mining frequent item sets for association rules. By using the discriminative power of the Class Association Rules we can build a classifier
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