A self acting initial seed selection algorithm for k-means clustering based on convex-hull

Communications in Computer and Information Science

Volumn 252 CCIS, Issue PART 2, 2011, Pages 641-650

  Shahnewaz, S M ^a   Rahman, Md Asikur ^b   Mahmud, Hasan ^a  

^a ISLAMIC UNIVERSITY OF TECHNOLOGY   (Bangladesh)

^b MEMORIAL UNIVERSITY OF NEWFOUNDLAND   (Canada)

Author keywords

Clustering; Convex hull; K means algorithm

Indexed keywords

CLUSTER-MERGING; CLUSTERING; CONVEX-HULL; DATA SETS; INFORMATION GAIN; INPUT SET; K-MEANS ALGORITHM; K-MEANS CLUSTERING; LOCAL OPTIMAL; NUMERICAL EXPERIMENTS; SEED SELECTION; TWO DIMENSIONAL SPACES;

INFORMATION SCIENCE; OPTIMAL SYSTEMS; OPTIMIZATION;

CLUSTERING ALGORITHMS;

EID: 82955176906     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-25453-6_54     Document Type: Conference Paper

References (13)

1. Anderberg, M.R.: Cluster Analysis for Applications. Academic Press, New York (1973)
2. Likas, A., Vlassis, N., Verbeek, J.J.: The global k-means clustering algorithm. Pattern Recognition 36, 451-461 (2003)
3. Khan, S.S., Ahmad, A.: Cluster center initialization algorithm for k-means clustering. Pattern Recognition Lett. 25(11), 1293-1302 (2004)
4. Mitra, P., Murthy, C.A., Pal, S.K.: Density-based multiscale data condensation. IEEE Trans. Pattern Anal. Machine Intell. 24(6), 734-747 (2002)
5. RizmanŽalik, K.: An efficient k'-means clustering algorithm. Pattern Recognition Letters 29(9) (2008)
6. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data. In: An Introduction to Cluster Analysis. Wiley, Canada (1990)
7. He, J., Lan, M., Tan, C.-L., Sung, S.-Y., Low, H.-B.: Initialization of cluster refinement algorithms: A review and comparativestudy. In: Proc. of Internat. Joint Conf. on Neural Networks (IJCNN), Budapest, Hungary, vol. 1, pp. 297-302 (2004)
8. Jiang, H., Yi, S., Li, J., Yang, F., Hu, X.: Ant clustering algorithm with K-harmonic means clustering. Expert Systems with Applications 37(12), 8679-8684 (2010)
9. Peña, J.M., Lozano, J.A., Larranaga, P.: An empirical comparison of four initialization methods for the K-Means algorithm. Pattern Recognition Letters 20, 1027-1040 (1999)
10. Newman, D.J., Hettich, S., Blake, C., Merz, C.J.: UCI repository ofmachine learning databases (1998), http://www.ics.uci.edu/~mlearn/ MLRepository.html
11. Bradford, C., AberDavid, B.: The Quickhull Algorithm forConvex Hulls. ACM Transactions on Mathematical Software 22(4) (1996)
12. Pearson, R.K., Zylkin, T., Schwaber, J.S., Gonye, G.E.: Quantitative evaluation of clustering results usingcomputational negative controls. In: Proc. 2004 SIAM International Conference on Data Mining, Lake Buena Vista, Florida (2004)
13. Bradley, P.S., Fayyad, U.M.: Refining initial points for k-means clustering. In: Proc. 15th Internat. Conf. on Machine Learning, pp. 91-99. Morgan Kaufmann, San Francisco (1998)