Modified Cohort Intelligence for Solving Machine Learning Problems

• Published in: Cohort Intelligence: A Socio-inspired Optimization Method Vol. 114; pp. 39 - 54
• Main Authors: Krishnasamy, Ganesh, Kulkarni, Anand Jayant, Abraham, Ajith
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2017; Springer International Publishing
• Series: Intelligent Systems Reference Library
• Subjects: Artificial intelligence; Cluster Problem; Local Optimum Problem; Particle Swarm Optimization; Solve Cluster Problem; Tabu Search
• ISBN: 3319442538; 9783319442532
• ISSN: 1868-4394; 1868-4408
• DOI: 10.1007/978-3-319-44254-9_4

Clustering is an important and popular technique in data mining. It partitions a set of objects in such a manner that objects in the same clusters are more similar to each another than objects in the different cluster according to certain predefined criteria. K-means is simple yet an efficient method used in data clustering. However, K-means has a tendency to converge to local optima and depends on initial value of cluster centers. In the past, many heuristic algorithms have been introduced to overcome this local optima problem. Nevertheless, these algorithms too suffer several short-comings. In this chapter, we present an efficient hybrid evolutionary data clustering algorithm referred as to K-MCI, whereby, we combine K-means with modified cohort intelligence.