Title: Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm

Authors: S. Jagadeesh Soundappan; R. Sugumar

Addresses: Department of CSE, St. Peter's University, India ' Department of Computer Science and Engineering, Velammal Institute of Technology, Chennai, India

Abstract: We present a framework that we are currently developing, that allows one to extract knowledge from the knowledge discovery in database (KDD) dataset. Data mining is a very active and space growing research area. Knowledge discovery in databases (KDD) is very useful in scientific domains. In simple terms, association rule mining is one of the most well-known methods for such knowledge discovery. Initially, database are divided into training and testing for the aid of fuzzy generating the rules using fuzzy rules generation the set of rules are generated from the given dataset. From the generated rules, we are extracting the significant rules by using the improved artificial bee colony algorithm and cuckoo search algorithm (IABCCS). After extracting optimal knowledge from the dataset via rules, the data will be classified using fuzzy classifier with the aid of this finally we will classify the attack and normal.

Keywords: knowledge discovery; data mining; scientific domains; fuzzy logic; association rules mining; artificial bee colony; ABC; cuckoo search; optimisation; knowledge extraction; fuzzy classifiers.

DOI: 10.1504/IJBIDM.2016.082213

International Journal of Business Intelligence and Data Mining, 2016 Vol.11 No.4, pp.338 - 356

Received: 26 Jul 2016
Accepted: 04 Nov 2016
Published online: 12 Feb 2017 *

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