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Enhancing K-means algorithm for solving classification problems
(2013-11-25)
Thammano, Arit
;
Kesisung, Pannee
K-means is the most popular clustering algorithm because of its efficiency and superior performance. However, the performance of K-means algorithm depends heavily on the selection of initial centroids. This paper proposes an extension to the original K-means algorithm enabling it to solve classification problems. First, the entropy concept is employed to adapt the traditional K-means algorithm to be used as a classification technique. Then, to improve the performance of K-means algorithm, a new scheme to select the initial cluster centers is proposed. The proposed models are tested on seven benchmark data sets from the UCI machine learning repository. Experimental results have shown that the proposed models outperform the learning vector quantization network in most of the tested data sets. © 2013 IEEE.
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A new computational intelligence technique based on human group formation
(2010-03-01)
Thammano, Arit
;
Moolwong, Jittraporn
This paper proposes a novel computational intelligence technique, based on the sociological concept of human group formation, with the aim to acquire a better solution to classification problems. The key concept of the human group formation is about the behavior of in-group members that try to unite with their own group as much as possible, and at the same time maintain social distance from the out-group members. This study compares the performance of the proposed model with that of fuzzy ARTMAP, radial basis function network, and learning vector quantization. Experimental results demonstrate the potential of the proposed approach in offering an efficient and effective solution to the problem. © 2009 Elsevier Ltd. All rights reserved.
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Feedforward neural network with multi-valued connection weights
(2009-09-11)
Thammano, Arit
;
Ruxpakawong, Phongthep
This paper introduces a new concept of the connection weight to the multi-layer feedforward neural network. The architecture of the proposed approach is the same as that of the original multi-layer feedforward neural network. However, the weight of each connection is multi-valued, depending on the value of the input data involved. The backpropagation learning algorithm was also modified to suit the proposed concept. This proposed model has been benchmarked against the original feedforward neural network and the radial basis function network. The results on six benchmark problems are very encouraging. © 2009 Springer Berlin Heidelberg.
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Solving classification problems using supervised self-organizing map
(2007-12-01)
Thammano, Arit
;
Kiatwuthiamorn, Jiraporn
This paper proposes the new approach to deal with the classification problems by modifying the well-known Kohonen self-organizing map in order to make it able to solve classification problems. During training, the fuzzy membership function is used in place of the Euclidean distance to find the best matching cluster for the input pattern. In order to improve the efficiency of proposed model, the fuzzy entropy concept is employed to reduce the number of nodes in the cluster layer. The performance of the proposed model was compared with the fuzzy ARTMAP neural network. The results on five benchmark problems are very encouraging. ©2007 IEEE.

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