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Heuristic Approaches in Clustering Problems

Heuristic Approaches in Clustering Problems
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Author(s): Onur Doğan (Istanbul Technical University, Turkey)
Copyright: 2018
Pages: 18
Source title: Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems
Source Author(s)/Editor(s): Ömer Faruk Yılmaz (Istanbul Technical University, Turkey & Yalova University, Turkey) and Süleyman Tüfekçí (University of Florida, USA)
DOI: 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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