Year-end Sale % OFF 
Abstract
Clustering is an important technique for discovering the inherent structure in a given data set without any ‘priori’ knowledge. Fuzzy clustering analysis is to assign objects to a given number of clusters with respect to some criteria such that each object may belong to more than one cluster with different degrees of membership. In this article, a new fuzzy clustering method based on tabu search called Improved Tabu Search Fuzzy Clustering (ITSFC) is proposed to find the proper clustering of data sets. In the ITSFC approach, a fuzzy c-means operation is developed to fine-tune the clustering solution obtained in the process of iterations and a divide-and-merge operation is designed to establish the neighborhood. Experimental results on two artificial and four real life data sets are given to illustrate the superiority of the proposed algorithm over a tabu search clustering algorithm and an artificial bee colony clustering algorithm. Key words: Fuzzy clustering, tabu search, artificial bee colony.
Highlights
Clustering is an unsupervised process that divides a given set of objects into groups so that objects within a cluster are similar with one another and dissimilar with the objects in other clusters
After reviewing the related work, we find that many research works focus on employing tabu search to solve the hard clustering problem (Al-sultan, 1995; Liu et al, 2008; Sung and Jin, 2000), but relatively few attempts have been made to solve the fuzzy clustering problem with tabu search
The Improved Tabu Search Fuzzy Clustering (ITSFC) algorithm observes the architecture of tabu search, integrates a one-step fuzzy c-means algorithm as the fuzzy cmeans operation to improve the current solution and accelerate the convergence speed of the clustering method, and designs the divide-and-merge operation to modulate the object distribution among different clusters and create the set of neighboring solutions
Summary
Clustering is an unsupervised process that divides a given set of objects into groups so that objects within a cluster are similar with one another and dissimilar with the objects in other clusters. It has been applied across many disciplines such as machine learning, pattern recognition and statistics (Pedrycz, 2005; Xu and Wunsch, 2008). Many clustering algorithms have been reported and they can be divided into two main categories: hierarchical and partitional (García-Escudero et al, 2010; Omran et al, 2007). Partitional clustering algorithms determine the clustering solution by maximizing the similarities among objects within the same group while minimizing the dissimilarities between different groups.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
