Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms
International Journal of Intelligent Information Systems
Volume 3, Issue 6-1, December 2014, Pages: 91-97
Received: Oct. 10, 2014; Accepted: Oct. 14, 2014; Published: Nov. 6, 2014
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Authors
Vahid Nouri, Department of Computer Engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran
Mohammad Reza Akbarzadeh, Department of Electrical Engineering, University of Neyshabur, Neyshabur, Iran
Tootoonchi, Alireza Rowhanimanesh, Departments of Electrical and Computer Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran
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Abstract
In several papers, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification results. A strong clustered dataset as input to classification algorithms can significantly improve the computation time. This can be particularly useful in “Big Data” where computation time is equally or more important than accuracy. However, there is a trade-off between computation time (speed) and accuracy among clustering algorithms. Specifically, general type-2 fuzzy c-means (GT2 FCM) is considered to be a highly accurate clustering approach, but it is computationally intensive. To improve its computation time we propose a hybrid clustering algorithm called KFGT2FCM that combines GT2 FCM with two fast algorithms k-means and Fuzzy C-means algorithm for input data preprocessing of classification algorithms. The proposed algorithm shows improved computation time when compared with GT2 FCM on five benchmarks from university of California Irvine (UCI) library.
Keywords
Classification, Input Data Preprocessing, Clustering, General Type-2 Fuzzy Logic, Fuzzy C-Means (FCM), K-Means
To cite this article
Vahid Nouri, Mohammad Reza Akbarzadeh, Tootoonchi, Alireza Rowhanimanesh, Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms, International Journal of Intelligent Information Systems. Special Issue: Research and Practices in Information Systems and Technologies in Developing Countries. Vol. 3, No. 6-1, 2014, pp. 91-97. doi: 10.11648/j.ijiis.s.2014030601.27
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