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Heuristic Approaches in Clustering Problems

Heuristic Approaches in Clustering Problems

Onur Doğan
Copyright: © 2018 |Pages: 18
ISBN13: 9781522529446|ISBN10: 1522529446|EISBN13: 9781522529453
DOI: 10.4018/978-1-5225-2944-6.ch006
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MLA

Doğan, Onur. "Heuristic Approaches in Clustering Problems." Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems, edited by Ömer Faruk Yılmaz and Süleyman Tüfekçí, IGI Global, 2018, pp. 107-124. https://doi.org/10.4018/978-1-5225-2944-6.ch006

APA

Doğan, O. (2018). Heuristic Approaches in Clustering Problems. In Ö. Faruk Yılmaz & S. Tüfekçí (Eds.), Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems (pp. 107-124). IGI Global. https://doi.org/10.4018/978-1-5225-2944-6.ch006

Chicago

Doğan, Onur. "Heuristic Approaches in Clustering Problems." In Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems, edited by Ömer Faruk Yılmaz and Süleyman Tüfekçí, 107-124. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2944-6.ch006

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Abstract

Clustering is an approach used in data mining to classify objects in parallel with similarities or separate according to dissimilarities. The aim of clustering is to decrease the amount of data by grouping similar data items together. There are different methods to cluster. One of the most popular techniques is K-means algorithm and widely used in literature to solve clustering problem is discussed. Although it is a simple and fast algorithm, there are two main drawbacks. One of them is that, in minimizing problems, solution may trap into local minimum point since objective function is not convex. Since the clustering is an NP-hard problem and to avoid converging to a local minimum point, several heuristic algorithms applied to clustering analysis. The heuristic approaches are a good way to reach solution in a short time. Five approaches are mentioned briefly in the chapter and given some directions for details. For an example, particle swarm optimization approach was used for clustering problem. In example, iris dataset including 3 clusters and 150 data was used.

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