Association Rule Mining Using Apriori Algorithm
Association Rule Mining using Apriori Algorithm
Ghanshyam Verma, Shruthi Varadhan
Computer Technology Department
KITS-Ramtek, Nagpur-441106 gs.verma@live.com shruthivaradhan@gmail.com
Abstract— Data mining is the process that results in the discovery of new patterns in large data sets. Data mining involves six common classes of tasks: Anomaly detection, Association
Rule Mining, clustering, classification, regression and summarization. Association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. Association rules are employed today in many application areas including Web usage mining, intrusion detection and bioinformatics. Apriori is a classic algorithm for learning association rules. Apriori is designed to operate on databases containing transactions. As is common in association rule mining, given a set of itemsets, the algorithm attempts to find subsets which are common to at least a minimum number C of the itemsets. Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation), and groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found. The quest to mine frequent patterns appears in many domains. The prototypical application is market basket analysis, i.e., to mine the sets of items that are frequent bought together . What makes
Apriori so popular is that uses the downward closure property of pattern support (all subsets of a frequent pattern must themselves be frequent) to prune the search space. Thus only frequent patterns of size k are used to generate patterns of size k+1. Many parallel data mining algorithms inherits this property from Apriori, which is why most parallel data mining algorithms are said to be a variation of Apriori.
Keywords— Data Mining, anomaly detection, Association Rule
Mining, clustering,
Ghanshyam Verma, Shruthi Varadhan
Computer Technology Department
KITS-Ramtek, Nagpur-441106 gs.verma@live.com shruthivaradhan@gmail.com
Abstract— Data mining is the process that results in the discovery of new patterns in large data sets. Data mining involves six common classes of tasks: Anomaly detection, Association
Rule Mining, clustering, classification, regression and summarization. Association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. Association rules are employed today in many application areas including Web usage mining, intrusion detection and bioinformatics. Apriori is a classic algorithm for learning association rules. Apriori is designed to operate on databases containing transactions. As is common in association rule mining, given a set of itemsets, the algorithm attempts to find subsets which are common to at least a minimum number C of the itemsets. Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation), and groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found. The quest to mine frequent patterns appears in many domains. The prototypical application is market basket analysis, i.e., to mine the sets of items that are frequent bought together . What makes
Apriori so popular is that uses the downward closure property of pattern support (all subsets of a frequent pattern must themselves be frequent) to prune the search space. Thus only frequent patterns of size k are used to generate patterns of size k+1. Many parallel data mining algorithms inherits this property from Apriori, which is why most parallel data mining algorithms are said to be a variation of Apriori.
Keywords— Data Mining, anomaly detection, Association Rule
Mining, clustering,
References: [1] [2] [3] [4] Association Rules Mining: A Recent Overview, Sotiris Kotsiantis, Dimitris Kanellopoulos, Educational Software Development Laboratory, D epartment of Mathematics, University of Patras, Greece. Tata McGraw H ill, the complete reference-java -j2se 5 edition. http://en.wikipedia.org/wiki/Data_mining, July 10, 2012. http://en.wikipedia.org/wiki/Association_rule_learning, July 10, 2012.