A new density-based scheme for clustering based on genetic algorithm

Fundamenta Informaticae

Volumn 68, Issue 4, 2005, Pages 315-331

  Lin, Chih Yang ^a   Chang, Chin Chen ^a   Lin, Chia Chen ^b  

^a NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

^b PROVIDENCE UNIVERSITY   (Taiwan)

Author keywords

Clustering algorithms; DBSCAN; Genetic algorithms

Indexed keywords

MATHEMATICAL MODELS; PARAMETER ESTIMATION;

CLUSTERING ALGORITHMS; DBSCAN;

GENETIC ALGORITHMS;

EID: 33645313810     PISSN: 01692968     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (18)

1. I. Rigoutsos and A. Califano: Searching in Parallel for Similar Strings, IEEE Computational Science and Engineering, vol. 1, pp. 60-75, 1994.
2. A. Joshi, N. Ramakrishman, E. N. Houstis, and J. R. Rice: On Neurobiological, Neuro-Fuzzy, Machine Learning and Statistical Pattern Recognition Techniques, IEEE Transactions on Neural Networks, vol. 8, pp. 18-31, 1997.
3. J. Grabmeier and A. Rudolph: Techniques of Cluster Algorithms in Data Mining, Data Mining and Knowledge Discovery, vol. 6, pp. 303-360, 2002.
4. J. Han and M. Kamber: Data Mining: Concepts and Techniques, San Francisco: Morgan Kaufmann, 2001.
5. J. MacQueen: Some Methods for Classification and Analysis of Multivariate Observations, Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, USA, 1967.
6. L. Kaufman and P. J. Rousseeuw: Finding Groups in Data: An Introduction to Clustering Analysis, John Wiley & Sons Inc., 1990.
7. M. Ester, H. P. Kregel, J. Sander, and X. Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, U.S.A., 1996.
8. W. Wang, J. Yang, and R. R. Muntz: STING: A Statistical Information Grid Approach to Spatial Data Mining, Proceedings of 23rd International Conference on Very Large Data Bases, Athens, Greece, 1997.
9. J. W. Shavlik and T. G. Dietterich: Readings in Machine Learning, San Mateo, CA: Morgan Kaufmann, 1990.
10. J. H. Holland: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press, 1992.
11. D. E. Goldberg: Genetic Algorithms in Search, Optimization and Machine Learning: Addison-Wesley, 1989.
12. M. Gen and R. Cheng: Genetic Algorithms and Engineering Optimization, John Wiley & Sons, Inc., 2000.
13. P. Mitra, C. A. Murthy, and S. K. Pal: Density-based Multiscale Data Condensation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 734-747, 2002.
14. D. Chaudhuri, C. A. Murthy, and B. B. Chaudhuri: Finding a Subset of Representative Points in a Dataset, IEEE Transactions on Systems, Man, and Cybernetics, vol. 24, pp. 1416-1424, 1994.
15. K. Fukunaga and J. M. Mantock: Nonparametric Data Reduction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, pp. 115-118, 1984.
16. L. Xu, A. Krzyzak, and E. Oja: Rival Penalised Competitive Learning for Cluster Analysis, RBF Net and Curve Detection, IEEE Transactions on Neural Networks, vol. 4, pp. 636-649, 1993.
17. Y. Leung, J. S. Zhang, and Z. B. Xu: Clustering by Scale-Space Filtering, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 1396-1410, 2000.
18. Z. Michalewicz: Genetic Algorithms + Data Structures = Evolution Programs, 3ed, Springer Verlag, 1996.