Abstract
Iterative methods and genetic algorithms have been used separately to minimize the loss function of representative-based clustering formulations. Neither of them alone seems to be significantly better. Moreover, the trade-off of effort versus quality slightly favors gradient descent. We present a unifying view for the three most popular loss functions: least sum of squares, its fuzzy version and the log likelihood function. We identify commonalities in gradient descent algorithms for the three loss functions and the evaluation of the loss function itself. We can then construct hybrids (genetic algorithms with a mutation operation that performs few gradient descent steps) for all three clustering approaches. We demonstrate that these hybrids are much efficient and effective (significantly render better performance as normalized by the number of function evaluations or CPU time).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
M.S. Aldenderfer and R.K. Blashfield. Cluster Analysis. Sage, Beverly Hills, 1984.
C.K. Chak and Feng G. Accelerated genetic algorithms: combined with local search for fast and accurate global search. In 1995 IEEE Int. Conf. Evolutionary Computation, 378–383, NY, 1995. IEEE Neural Network Council.
V. Cherkassky and F. Muller. Learning from Data — Concept, Theory and Methods. Wiley, NY, 1998.
V. Estivill-Castro and A.T. Murray. Clustering and capacitated facility location via hybrid optimisation. Proc. Int. ICSC Symp. on Intelligent Systems and Applications ISA-WOO, Wollongong, Australia, Dec. 12–15 2000.
V Estivill-Castro and R. Torres-Velázquez. Hybrid genetic algorithm for solving the p-median problem. A Yao, et al eds., Proc. SEAL-98, 18–25. Springer Verlag LNAI 1585, 1999.
I.B. Hall, L.O. Özyurt and J.C. Bezdek. Clustering with a genetically optimized approach. IEEE T. Evolutionary Computation, 3(2): 103–112, 1999.
C.L. Huntley and Brown D.E. Parallel genetic algorithms with local search. Computer Ops Res, 26(6):559–571, 1996.
K. Koperski, J. Han, and J. Adhikari. Mining knowledge in geographical data. Communications of the ACM. to appear.
A.C. Kwong, S. abd Ng and K.F. Man. Improving local search in genetic algorithms for numerical global optimization using modified grid-point search technique. In Proc. GALESIA, 419–423, London, UK, 1995. IEE.
J.J. Oliver, R.A. Baxter, and C.S. Wallace. Unsupervised learning using MML. Proc. 13th Machine Learning Conf., 364–372, 1996. Morgan Kaufmann.
I. Ono and S. Kobayashi. A real coded genetic algorithm for function optimization using unimodal normal distributed crossover. T. Back, ed., Proc. 7th Int. Conf. on Genetic Algorithms, 246–253, 1997.
C. Reeves. Hybrid genetic algorithms for bin-packing and related problems. Annals of Operations Research, 63:371–396, May 1996.
P.J. Rousseeuw and A.M. Leroy. Robust regression and outlier detection. Wiley, NY, 1987.
M.A. Tanner. Tools for Statistical Inference. Springer-Verlag, NY, US., 1993.
N.L.J. Ulder, E.H.L. Aarts, H.-J. Bandelt, P.J.M. van Laarhoven, and E. Pesch. Genetic local search algorithms for the travelling salesman problem. H.-P. Schwefel et al, eds. Proc. 1st Workshop on Parallel Problem Solving from Nature, 109–116, Berlin, Germany, 1991. IEEE, Springer Verlag.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Estivill-Castro, V. (2000). Hybrid Genetic Algorithms Are Better for Spatial Clustering. In: Mizoguchi, R., Slaney, J. (eds) PRICAI 2000 Topics in Artificial Intelligence. PRICAI 2000. Lecture Notes in Computer Science(), vol 1886. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44533-1_44
Download citation
DOI: https://doi.org/10.1007/3-540-44533-1_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67925-7
Online ISBN: 978-3-540-44533-3
eBook Packages: Springer Book Archive