Minimizing Frequent Itemsets Using Hybrid ABCBAT Algorithm
The expansion in information technology field leads to the increase in amount of data collected. Huge amount of data is stored in databases, data warehouses and repositories. Data mining is the process of analyzing the database and extract the required in
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Abstract The expansion in information technology field leads to the increase in amount of data collected. Huge amount of data is stored in databases, data warehouses and repositories. Data mining is the process of analyzing the database and extract the required information and finding the relationships among the items of datasets using association rule mining. Apriori is a familiar algorithm for association rule mining which generates frequent itemsets. In this paper, we propose a new algorithm called hybrid ABCBAT which minimizes the generation of frequent itemsets and also reduces the time, space and memory. In the proposed algorithm, ABC is hybridized with random walk of BAT algorithm. Random walk is used in the place of onlooker bee phase in order to increase the exploration. Hybrid ABCBAT algorithm is applied over the frequent itemsets gathered from apriori algorithm, to minimize frequent itemsets. Different datasets from UCI repository are considered for experiment. The proposed algorithm has better optimization accuracy, convergence rate and robustness. Keywords Apriori algorithm algorithm Frequent itemsets
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Association rule mining Random walk
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Artificial bee colony
S. Neelima (✉) Department of CSE, JNTUH, Hyderabad, India e-mail: [email protected] N. Satyanarayana Department of CSE, Nagole Institute of Technology and Science, Hyderabad, India e-mail: [email protected] P. Krishna Murthy Swarna Bharathi Institute of Science and Technology, Khammam, Andhra Pradesh, India e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 S.C. Satapathy et al. (eds.), Data Engineering and Intelligent Computing, Advances in Intelligent Systems and Computing 542, DOI 10.1007/978-981-10-3223-3_9
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1 Introduction In recent years the size of the database is rapidly increasing. This led to a growing interest in the development of tools capable of extracting knowledge from data. Data mining is the process of analyzing large amounts of data and extracting useful information. Data mining functionalities include classification, clustering, link analysis (association) and prediction. Association rule mining is one of the important data mining techniques which find the relationship between the set of items in the database [1]. The association rules were first proposed by R.Agrawal in 1993 for mining frequent itemsets. Set of items which has minimum support are frequent itemsets. Association rule mining include two major tasks: Generating the frequent itemsets using minimum support that occur in the datasets and generation of strong association rules by using these frequent itemsets [2]. Hence, generation of frequent itemsets is the major task of association rule mining. There are number of techniques to generate the frequent itemsets. Apriori is an innovative algorithm for finding frequent itemsets from the huge amount of data.
2 Apriori Algorithm For mining frequent itemsets for Boolean association rules R. Agrawal and R. Srikant in 1994 initiated the apriori algorith
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