Pattern Discovery Using Apriori and Ch-Search Algorithm

The association rule describes and discovers the relations between variables in large databases. Generally association rules are based on the support and confidence framework. In apriori algorithm, association rules are used for finding minimum support & minimum confidence. But this method can lead to loss of rules i.e. negative rules are lost. Hence Ch-Search algorithm is introduced which uses its strongest rule i.e. commonly used the Coherent rule which promotes information correctness and leads to appropriate decision making among the same item sets. The coherent rule discovers positive as well as negative outcomes, and does not require finding minimum support & minimum confidence frameworks leading to no loss of any rules. The paper describes how the Ch algorithm is used by coherent rule for exact pattern discovery in large databases over apriori algorithm.