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Visual Discovery of Network Patterns of Interaction between Attributes

  • Simeon J. Simoff
  • John Galloway
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4404)

Abstract

Visual discovery of network patterns of interaction between attributes in a data set identifies emergent networks between myriads of individual data items and utilises special algorithms that aid visualisation of ‘emergent’ patterns and trends in the linkage. It complements conventional data mining methods, which assume the independence between the attributes and the independence between the values of these attributes. The approach complements analytical data mining techniques where the rules or definitions of what might constitute an exception are able to be known and specified ahead of time. For example, in the analysis of transaction data there are no known suspicious transactions. This chapter presents a human-centred visual data mining methodology that addresses the issues of depicting implicit relationships between data attributes and/or specific values of these attributes. Different aspects of the approach is demonstrated through the reflection of the analytical process in two cases: one looking at fraudulent activity which will be difficult, if not impossible to detect with conventional exception detection methods, and the other one looking at exploring a large data set of low level communication data. The chapter argues that for many problems, a ‘discovery’ phase in the investigative process based on visualisation and human cognition is a logical precedent to, and complement of, more automated ‘exception detection’ phases.

Keywords

Data Mining Data Item Social Network Analysis Data Mining Method Network Pattern 
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.

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References

  1. 1.
    Klösgen, W., Zytkow, J.M. (eds.): Handbook of Data Mining and Knowledge Discovery, p. 1064. Oxford University Press, Oxford (2002)zbMATHGoogle Scholar
  2. 2.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: An overview. In: Fayyad, U.M., et al. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 1–34. AAAI Press/The MIT Press, Cambridge, Massachusetts (1996)Google Scholar
  3. 3.
    Berthold, M., Hand, D.J. (eds.): Intelligent Data Analysis: An Introduction, 2nd edn., p. 514. Springer, New York (2003)Google Scholar
  4. 4.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. In: Gray, J. (ed.) The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann Publishers, San Francisco (2006)Google Scholar
  5. 5.
    Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. The MIT Press, Cambridge (2001)Google Scholar
  6. 6.
    Weiss, S.M., Zhang, T.: Performance analysis and evaluation. In: Nong, Y. (ed.) The Handbook of Data Mining. Lawrence Erlbaum Associates, New Jersey (2003)Google Scholar
  7. 7.
    Scott, J.: Social Network Analysis: A Handbook, 2nd edn. Sage Publications, London (2000)Google Scholar
  8. 8.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)Google Scholar
  9. 9.
    Dunham, M.H.: Data Mining: Introductory and Advanced Topics. Prentice-Hall, Englewood Cliffs (2002)Google Scholar
  10. 10.
    Schwartz, M.E., Wood, D.C.M.: Discovering shared interests using graph analysis. Communications of ACM 36(8), 78–89 (1993)CrossRefGoogle Scholar
  11. 11.
    Scott, J.: Social Network Analysis: A Handbook, 2nd edn. Sage Publications, London (2000)Google Scholar
  12. 12.
    Albert, R., Barabási, A.-L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47–97 (2002)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Borgatti, S.P.: The network paradigm in organizational research: A review and typology. Journal of Management 29(6), 991–1013 (2003)CrossRefGoogle Scholar
  15. 15.
    Batagelj, V., Mrvar, A.: Pajek - Analysis and visualization of large networks. In: Juenger, M., Mutzel, P. (eds.) Graph Drawing Software. LNCS, vol. 2265, pp. 77–103. Springer, Heidelberg (2003)Google Scholar
  16. 16.
    Kleinberg, J.: The wireless epidemic. Nature 449, 287–288 (2007)CrossRefGoogle Scholar
  17. 17.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30(1-7), 107–117 (1998)CrossRefGoogle Scholar
  19. 19.
    Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: Proceedings CIKM 2003, November 3–8. ACM Press, New Orleans (2003)Google Scholar
  20. 20.
    Leskovec, J., Singh, A., Kleinberg, J.: Patterns of Influence in a Recommendation Network. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 380–389. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining. ACM Press, San Francisco (2001)Google Scholar
  22. 22.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining. ACM Press, Edmonton (2002)Google Scholar
  23. 23.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings ACM KDD 2003. ACM Press, Washington, DC (2003)Google Scholar
  24. 24.
    Nong, Y. (ed.): The Handbook of Data Mining, vol. 689. Lawrence Erlbaum Associates, New Jersey (2003)Google Scholar
  25. 25.
    Fayyad, U.M.: Editorial. ACM SIGKDD Explorations 5(2), 1–3 (2003)CrossRefGoogle Scholar
  26. 26.
    Shillabeer, A., Roddick, J.: Establishing a lineage for medical knowledge discovery. In: Proceedings of the Sixth Australasian Data Mining Conference (AusDM 2007). ACS, Gold Coast (2007)Google Scholar
  27. 27.
    Ramoni, M.F., Sebastiani, P.: Bayesian methods for intelligent data analysis. In: Berthold, M., Hand, D.J. (eds.) Intelligent Data Analysis: An Introduction, pp. 131–168. Springer, New York (2003)CrossRefGoogle Scholar
  28. 28.
    Schön, D.: The Reflective Practitioner. Basic Books, New York (1983)Google Scholar
  29. 29.
    Schön, D.: Educating The Reflective Practitioner. Jossey Bass, San Francisco (1991)Google Scholar
  30. 30.
    Pirolli, P., Card, S.: Sensemaking processes of intelligence analysts and possible leverage points as identified through cognitive task analysis. In: Proceedings of the 2005 International Conference on Intelligence Analysis, McLean, Virginia (2005)Google Scholar
  31. 31.
    Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: CRISP-DM 1.0: Step-by-step data mining guide, SPSS (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Simeon J. Simoff
    • 1
    • 2
  • John Galloway
    • 3
  1. 1.School of Computing and Mathematics, College of Health and ScienceUniversity of Western SydneyAustralia
  2. 2.Faculty of Information TechnologyUniversity of TechnologySydneyAustralia
  3. 3.Chief ScientistNetMap Analytics Pty LtdSt LeonardsAustralia

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