Improving RBF networks classification performance by using K-Harmonic means

World Academy of Science, Engineering and Technology

Volumn 62, Issue , 2010, Pages 982-986

  Zainuddin, Z ^a   Lye, W K ^a  

^a SCHOOL OF PHYSICS   (Malaysia)

Author keywords

Clustering method; K harmonic means; Neural networks; Radial basis functions

Indexed keywords

BASIS FUNCTIONS; BENCHMARK DATA; BREAST CANCER; CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; CLUSTER WELL; CLUSTERING METHODS; FUZZY C-MEANS; HARMONIC MEAN; K-MEANS; RADIAL BASIS FUNCTIONS; RBF NETWORK;

CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; HARMONIC ANALYSIS; HARMONIC FUNCTIONS; IMAGE SEGMENTATION; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS;

CLUSTERING ALGORITHMS;

EID: 78651570822     PISSN: 2010376X     EISSN: 20103778     Source Type: Journal    
DOI: None     Document Type: Article

References (11)

1. C.M. Bishop, Neural Networks for Pattern Recognition. OxfordUniversity Pres, New York, USA, 1995.
2. N. Benoudjit, M. Verleysen, On the kernel width in radial basis functionnetworks. Neural Processing Letters 18, 2003, 139-154.
3. J. Moody, C.J. Darken, Fast learning in networks of locally-tunedprocessing unit. Neural Computation 1, 1989, 281-294.
4. K. Warwick, J.D. Mason and E.L. Sutanto, Neural network basisfunction center selection using cluster analysis. Proceeding of AmericanCentral Conference, Washington, June, 1995
5. J.C. Dunn, A fuzzy relative of the ISODATA process and its use ndetecting 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 thefuzzy cmeans clustering algorithms. IEEE Trans Pattern Arial Machine, Intell 8, 248-255.
8. B. Zhang, M. Hsu, U. Dayal, K-harmonic means-a data clusteringalgorithm, Technical Report HPL-1999-124, Hewlett-PackardLaboratories, 1999.
9. B. Zhang, M. Hsu, U. Dayal, K-harmonic means, in: InternationalWorkshop 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 findbetter clusterins, in: Proceedings of the 11th International Conference onInformation 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.