Improving RBF Networks Classification Performance by using K-Harmonic Means
In this paper, a clustering algorithm named KHarmonic
means (KHM) was employed in the training of Radial
Basis Function Networks (RBFNs). KHM organized the data in
clusters and determined the centres of the basis function. The popular
clustering algorithms, namely K-means (KM) and Fuzzy c-means
(FCM), are highly dependent on the initial identification of elements
that represent the cluster well. In KHM, the problem can be avoided.
This leads to improvement in the classification performance when
compared to other clustering algorithms. A comparison of the
classification accuracy was performed between KM, FCM and KHM.
The classification performance is based on the benchmark data sets:
Iris Plant, Diabetes and Breast Cancer. RBFN training with the KHM
algorithm shows better accuracy in classification problem.
[1] C.M. Bishop, Neural Networks for Pattern Recognition. Oxford
University Pres, New York, USA, 1995.
[2] N. Benoudjit, M. Verleysen, On the kernel width in radial basis function
networks. Neural Processing Letters 18, 2003, 139-154.
[3] J. Moody, C.J. Darken, Fast learning in networks of locally-tuned
processing unit. Neural Computation 1, 1989, 281-294.
[4] K.Warwick, J.D. Mason and E.L. Sutanto, Neural network basis
function center selection using cluster analysis. Proceeding of American
Central Conference, Washington, June, 1995
[5] J.C. Dunn, A fuzzy relative of the ISODATA process and its use n
detecting compact well-separated clusters. J. Cybernet. 3, 1973, 32-57.
[6] J. Bezdek, Pattern recognition with fuzzy objective function algorithm,
Plenum Pres, NewYork, 1981.
[7] R.L. Canon, J.Dave and J.C. Bezdek, Efficient implementation of the
fuzzy cmeans clustering algorithms. IEEE Trans Pattern Arial Machine,
Intell 8, 248-255.
[8] B. Zhang, M. Hsu, U. Dayal, K-harmonic means - a data clustering
algorithm, Technical Report HPL-1999-124, Hewlett -Packard
Laboratories, 1999.
[9] B. Zhang, M. Hsu, U. Dayal, K-harmonic means, in: International
Workshop on Temporal, Spatial and Spatio-Temporal Data Mining,
TSDM2000, Lyon, France, 12 September 2000.
[10] G. Hammerly, C. Elken, Alternatives to the K-means algorithm that find
better clusterins, in: Proceedings of the 11th International Conference on
Information and Knowledge Management, 2002, pp. 600-607.
[11] C.L. Blake, C.J. Merz, UCI repository of machine learning databases,
2008, http://archive.ics.uci.edu/ml/databases.html.
[1] C.M. Bishop, Neural Networks for Pattern Recognition. Oxford
University Pres, New York, USA, 1995.
[2] N. Benoudjit, M. Verleysen, On the kernel width in radial basis function
networks. Neural Processing Letters 18, 2003, 139-154.
[3] J. Moody, C.J. Darken, Fast learning in networks of locally-tuned
processing unit. Neural Computation 1, 1989, 281-294.
[4] K.Warwick, J.D. Mason and E.L. Sutanto, Neural network basis
function center selection using cluster analysis. Proceeding of American
Central Conference, Washington, June, 1995
[5] J.C. Dunn, A fuzzy relative of the ISODATA process and its use n
detecting compact well-separated clusters. J. Cybernet. 3, 1973, 32-57.
[6] J. Bezdek, Pattern recognition with fuzzy objective function algorithm,
Plenum Pres, NewYork, 1981.
[7] R.L. Canon, J.Dave and J.C. Bezdek, Efficient implementation of the
fuzzy cmeans clustering algorithms. IEEE Trans Pattern Arial Machine,
Intell 8, 248-255.
[8] B. Zhang, M. Hsu, U. Dayal, K-harmonic means - a data clustering
algorithm, Technical Report HPL-1999-124, Hewlett -Packard
Laboratories, 1999.
[9] B. Zhang, M. Hsu, U. Dayal, K-harmonic means, in: International
Workshop on Temporal, Spatial and Spatio-Temporal Data Mining,
TSDM2000, Lyon, France, 12 September 2000.
[10] G. Hammerly, C. Elken, Alternatives to the K-means algorithm that find
better clusterins, in: Proceedings of the 11th International Conference on
Information and Knowledge Management, 2002, pp. 600-607.
[11] C.L. Blake, C.J. Merz, UCI repository of machine learning databases,
2008, http://archive.ics.uci.edu/ml/databases.html.
@article{"International Journal of Information, Control and Computer Sciences:52052", author = "Z. Zainuddin and W. K. Lye", title = "Improving RBF Networks Classification Performance by using K-Harmonic Means", abstract = "In this paper, a clustering algorithm named KHarmonic
means (KHM) was employed in the training of Radial
Basis Function Networks (RBFNs). KHM organized the data in
clusters and determined the centres of the basis function. The popular
clustering algorithms, namely K-means (KM) and Fuzzy c-means
(FCM), are highly dependent on the initial identification of elements
that represent the cluster well. In KHM, the problem can be avoided.
This leads to improvement in the classification performance when
compared to other clustering algorithms. A comparison of the
classification accuracy was performed between KM, FCM and KHM.
The classification performance is based on the benchmark data sets:
Iris Plant, Diabetes and Breast Cancer. RBFN training with the KHM
algorithm shows better accuracy in classification problem.", keywords = "Neural networks, Radial basis functions, Clusteringmethod, K-harmonic means.", volume = "4", number = "2", pages = "206-5", }