A New Clustering Algorithm for Productivity in Data Mining: The Case of UCA Data

Abstract

Methods of clustering in data mining have dramatically developed in recent years as a result of the crucial need to categorize data leading to the expansion of data mining techniques and enhanced productivity of clustering methods in management and decision making. Whale optimization algorithm is a new stochastic global optimization method employed to resolve various problems. We already presented a data clustering method based on Whale optimization algorithm in which the initial solutions are randomly selected. What has made K-mean algorithm a highly popular clustering approaches appealing to many researchers is the simplicity and brevity of the stages involved in the process. The present enquiry aimed at employing K-mean algorithm to improve the capability of Whale optimization clustering and proposing the hybrid KWOA algorithm which can find more accurate clusters. The computational results of running the newly proposed algorithm, along with some well-known clustering algorithms, on real data sets from a well-known machine learning repository underscored the promising performance of the proposed algorithm in terms of the quality and standard deviation of the final solutions.

Keywords

• clustering
• data mining
• productivity
• swarm intelligence