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WEI-MING CHEN k-medoid clustering with genetic algorithm

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Outline k-medoids clustering famous works GCA : clustering with the add of a genetic algorithm Clustering genetic algorithm : also judge the number of clusters Conclusion

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k-medoids clustering famous works GCA : clustering with the add of a genetic algorithm Clustering genetic algorithm : also judge the number of clusters Conclusion

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What is k-medoid clustering? Proposed in 1987 (L. Kaufman and P.J. Rousseeuw) There are N points in the space k points are chosen as centers (medoids) Classify other points into k groups Which k points should be chosen to minimize the summation of the points to its medoid

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Difficulty NP-hard Genetic algorithms can be applied

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k-medoid clustering famous works GCA : clustering with the add of a genetic algorithm Clustering genetic algorithm : also judge the number of clusters Conclusion

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Partitioning Around Medoids (PAM) Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. New York: Wiley Group N data into k sets In every generation, select every pair of (O i, O j ), where O i is a medoid and O j is not, if replace O i by O j would reduce the distance, replace O i by O j Computation time : O(k(N-k) 2 ) [one generation]

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Clustering LARge Applications (CLARA) Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. New York: Wiley Reduce the calculation time Only select s data in original N data s = 40+2k seems a good choice Computation time : O(ks 2 +k(n-k)) [one generation]

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Clustering Large Applications based upon RANdomized Search (CLARANS) Ng, R., & Han, J. (1994). Efficient and effective clustering methods for spatial data mining. In Proceedings of the 20th international conference on very large databases, Santiago, Chile (pp. 144–155) Do not try all pairs of (O i, O j ) Try max(0.0125(k(N-k)), 250) different O j to each O i Computation time : O(N 2 ) [one generation]

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k-medoids clustering famous works GCA : clustering with the add of a genetic algorithm Clustering genetic algorithm : also judge the number of clusters Conclusion

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GCA Lucasius, C. B., Dane, A. D., & Kateman, G. (1993). On k-medoid clustering of large data sets with the aid of a genetic algorithm: Background, feasibility and comparison. Analytica Chimica Acta, 282, 647– 669.

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Chromosome encoding N data, clustering to k groups Problem size = k (the number of groups) each location of the string is an integer (1~N) (a medoid)

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Initialization Each string in the population uniquely encodes a candidate solution of the target problem Random choose the candidates

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Selection Select M worst individuals in population and throw them out

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Crossover Select some individuals for reproducing M new population Building-block like crossover Mutation

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Crossover For example, k =3, p 1 = 2 3 7, p 2 = Mix p 1 and p 2 Q = randomly scramble : Q = Add new material : first k elements may be changed Q = randomly scramble again Q = The offspring are selected from left or from right C 1 = 2 7 3, C 2 = 8 5 3

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Experiment Under the limit of NFE < N = 1000, k = 15

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Experiment GCA versus Random search

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Experiment GCA versus CLARA (k = 15)

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Experiment GCA versus CLARA (k = 50)

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Experiment

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Paper’s conclusion GCA can handle both large values of k and small values of k GCA outperforms CLARA, especially when k is a large value GCA lends itself excellently for parallelization GCA can be combined with CLARA to obtain a hybrid searching system with better performance.

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k-medoids clustering famous works GCA : clustering with the add of a genetic algorithm Clustering genetic algorithm : also judge the number of clusters Conclusion

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Motivation In some cases, we do not actually know the number of clusters If we only know the upper limit?

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Hruschka, E.R. and F.F.E. Nelson. (2003). “A Genetic Algorithm for Cluster Analysis.” Intelligent Data Analysis 7, 15–25.

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Fitness function

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Chromosome encoding N data, clustering to at most k groups Problem size = N+1 each location of the string is an integer (1~k) (belongs to which cluster ) Genotype1: To avoid following problems: Genotype2: 2|2222| Genotype3: 4|4444| Child2: Child3: Consistent Algorithm :

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Initialization Population size = 20 The ﬁrst genotype represents two clusters, the second genotype represents three clusters, the third genotype represents four clusters,..., and the last one represents 21 clusters

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Selection

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Crossover Uniform crossover do not work Use Grouping Genetic Algorithm (GGA), proposed by Falkenauer (1998) First, two strings are selected A − B − Randomly select groups to preserve in A (For example, group 2 and 3)

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Crossover A − B − C − Check the unchanged group in B and place in C C − Another child : form by the groups in B (without which is actually placed in C) D −

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Crossover A − B − C − Another child : form by the groups in B (without which is actually placed in C) D − Check the unchanged group in A and place in D The other objects (whose alleles are zeros) are placed to the nearest cluster

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Mutation Two ways for mutation 1. randomly chosen a group, places all the objects to the remaining cluster that has the nearest centroid 2. divides a randomly selected group into two new ones Just change the genotypes in the smallest possible way

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Experiment 4 test problems (N = 75, 200, 699, 150)

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Experiment Ruspini data (N = 75)

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Paper’s conclusion Do not need to know the number of groups Find out the answer of four different test problems successfully Only on small population size

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k-medoids clustering famous works GCA : clustering with the add of a genetic algorithm Clustering genetic algorithm : also judge the number of clusters Conclusion

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Genetic algorithms is an acceptable method for clustering problems Need to design crossover carefully Maybe EDAs can be applied Some theses? Or final projects!

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