Similar or Not Similar: This Is a Parameter Question

  • Andrey Araujo Masiero
  • Flavio Tonidandel
  • Plinio Thomaz Aquino Junior
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8016)

Abstract

There is much information of users to be analyzed to develop a personalized project. To perform an analysis, it is necessary to create clusters in order to identify features to be explored by the project designer. In general, a classical clustering algorithm called K-Means is used to group users features. However, K-Means reveals some problems during the cluster process. In fact, K-Means does not guarantee to find Quality-Preserved Sets (QPS) and its randomness let the entire process unpredictable and unstable. In order to avoid these problems, a novel algorithm called Q-SIM (Quality Similarity Clustering) is presented in this paper. The Q-SIM algorithm has the objective to keep a similarity degree among all elements inside the cluster and guarantee QPS for all sets. During the tests, Q-SIM demonstrates that it is better than k-means and it is more appropriate to solve the problem for user modeling presented in this paper.

Keywords

Q-SIM Clustering User Modeling Personas 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aquino Jr., P.T., Filgueiras, L.V.L.: A expressao da diversidade de usuarios no projeto de interacao com padroes e personas. In: Proceedings of the VIII Brazilian Symposium on Human Factors in Computing Systems, IHC 2008, pp. 1–10. Brazilian Computer Society, Porto Alegre (2008)Google Scholar
  2. 2.
    Aquino Jr., P.T., Filgueiras, L.V.L.: User modeling with personas. In: Proceedings of the 2005 Latin American Conference on Human-computer Interaction, CLIHC 2005, pp. 277–282. ACM, New York (2005)Google Scholar
  3. 3.
    Bezdek, J., Pal, N.: Cluster validation with generalized dunn’s indices. In: Proceedings of the Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, pp. 190–193 (November 1995)Google Scholar
  4. 4.
    Cooper, A.: The Inmates Are Running the Asylum. Macmillan Publishing Co. Inc., Indianapolis (1999)Google Scholar
  5. 5.
    Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1(2), 224–227 (1979)CrossRefGoogle Scholar
  6. 6.
    Dutta, M., Mahanta, A.K., Pujari, A.K.: Qrock: A quick version of the rock algorithm for clustering of categorical data. Pattern Recogn. Lett. 26(15), 2364 (2005)CrossRefGoogle Scholar
  7. 7.
    Garey, M.R., Johnson, D.S.: Computers and Intractability; A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York (1990)Google Scholar
  8. 8.
    Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)CrossRefGoogle Scholar
  9. 9.
    Jung, C.: The archetypes and the collective unconscious (1991)Google Scholar
  10. 10.
    Legany, C., Juhasz, S., Babos, A.: Cluster validity measurement techniques. In: Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases, AIKED 2006, pp. 388–393. World Scientific and Engineering Academy and Society, Stevens Point (2006)Google Scholar
  11. 11.
    Masiero, A.A., Leite, M.G., Filgueiras, L.V.L., Aquino Jr., P.T.: Multidirectional knowledge extraction process for creating behavioral personas. In: Proceedings of the 10th Brazilian Symposium on on Human Factors in Computing Systems and the 5th Latin American Conference on Human-Computer Interaction, IHC+CLIHC 2011, pp. 91–99. Brazilian Computer Society, Porto Alegre (2011)Google Scholar
  12. 12.
    Smyth, B., McKenna, E.: Competence guided incremental footprint-based retrieval. Knowledge-Based Systems 14, 155–161 (2001)CrossRefGoogle Scholar
  13. 13.
    Weber, I., Jaimes, A.: Who uses web search for what: and how. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 15–24. ACM, New York (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Andrey Araujo Masiero
    • 1
  • Flavio Tonidandel
    • 1
  • Plinio Thomaz Aquino Junior
    • 1
  1. 1.FEI University CenterS. Bernardo CampoBrasil

Personalised recommendations