Abstract
Generating Frequent Patterns from Large Datasets using Improved Apriori and Support Chaining Method
Highlights
A days Data mining has been widely used and unifies research in various fields such as computer science, networking and engineering, statistics, databases, machine learning and Artificial Intelligence etc
There are different techniques that fit in this category including association rule mining, classification and clustering as well as regression Apriori algorithm is the most efficient candidate generation approach proposed by Agrawal et al (1993)
The main objective of this study is to reduce the number of frequent item sets
Summary
A days Data mining has been widely used and unifies research in various fields such as computer science, networking and engineering, statistics, databases, machine learning and Artificial Intelligence etc. There are different techniques that fit in this category including association rule mining, classification and clustering as well as regression Apriori algorithm is the most efficient candidate generation approach proposed by Agrawal et al (1993). To count the support of item sets, it uses breadth-first search strategy and to utilize the downward closure property of support, it uses candidate generation function. Apriori algorithm is an iterative one known as level-wise search and it uses the prior knowledge of frequent item set properties in generating association rules (Agrawal et al, 1993). Apriori algorithm works with the following principle. If an item set is frequent, all of its subsets must be frequent
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