An Incremental Clustering Algorithm Based on Compact Sets with Radius α

  • Aurora Pons-Porrata
  • Guillermo Sánchez Díaz
  • Manuel Lazo Cortés
  • Leydis Alfonso Ramírez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


In this paper, we present an incremental clustering algorithm in the logical combinatorial approach to pattern recognition, which finds incrementally the β 0-compact sets with radius α of an object collection. The proposed algorithm allows generating an intermediate subset of clusters between the β 0-connected components and β 0-compact sets (including both of them as particular cases). The evaluation experiments on standard document collections show that the proposed algorithm outperforms the algorithms that obtain the β 0-connected components and the β 0-compact sets.


Cluster Algorithm Document Collection Cluster Criterion Incremental Algorithm Topic Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. [1]
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in knowledge discovery in databases. MIT Press, Cambridge (1996)Google Scholar
  2. [2]
    Jain, K., Dubes, R.: Algorithms for clustering data. Prentice Hall, Englewood Cliffs (1998)Google Scholar
  3. [3]
    Larsen, B., Aone, C.: Fast and Effective Text Mining Using Linear-time Document Clustering. In: Proceedings of KDD 1999, San Diego, California, pp. 16–22 (1999)Google Scholar
  4. [4]
    Lopez-Caviedez, M.: A cities stratification tool in risk zones for the healt. MSc. Thesis, UAEH, Pachuca, Hgo. Mexico (2004) (in Spanish)Google Scholar
  5. [5]
    Lopez-Caviedez, M., Sanchez-Díaz, G.: A new clustering criterion in pattern recognition. WSEAS Transactions on Computers 3(3), 558–562 (2004)Google Scholar
  6. [6]
    Martínez Trinidad, J.F., Ruiz Shulcloper, J., Lazo Cortés, M.: Structuralization of universes. Fuzzy Sets and Systems 112(3), 485–500 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  7. [7]
    Pons-Porrata, A., Berlanga-Llavori, R., Ruiz-Shulcloper, J.: On-line event and topic detection by using the compact sets clustering algorithm. Journal of Intelligent and Fuzzy Systems (3-4), 185–194 (2002)Google Scholar
  8. [8]
    Sanchez-Díaz, G., Ruiz-Shulcloper, J.: Mid mining: a logical combinatorial pattern recognition approach to clustering in large data sets. In: Proc. VI Ibero-American Symposium on Pattern Recognition, Lisboa, Portugal, pp. 475–483 (2000)Google Scholar
  9. [9]
    Sarker, R., Abbass, H., Newton, C.: Introducing data mining and knowledge discovery. In: Heuristics & optimization for knowledge discovery, pp. 1–12. Idea Group publishing (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Aurora Pons-Porrata
    • 1
  • Guillermo Sánchez Díaz
    • 2
  • Manuel Lazo Cortés
    • 3
  • Leydis Alfonso Ramírez
    • 1
  1. 1.Center of Pattern Recognition and Data MiningUniversity of OrienteSantiago de CubaCuba
  2. 2.Center of Technologies Research on Information and SystemsUAEHPachuca, HgoMexico
  3. 3.Institute of CyberneticsMathematics and PhysicsHavanaCuba

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