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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Klösgen, W., Zytkow, J.M. (eds.): Handbook of Data Mining and Knowledge Discovery, p. 1064. Oxford University Press, Oxford (2002)
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)
Berthold, M., Hand, D.J. (eds.): Intelligent Data Analysis: An Introduction, 2nd edn., p. 514. Springer, New York (2003)
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)
Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. The MIT Press, Cambridge (2001)
Weiss, S.M., Zhang, T.: Performance analysis and evaluation. In: Nong, Y. (ed.) The Handbook of Data Mining. Lawrence Erlbaum Associates, New Jersey (2003)
Scott, J.: Social Network Analysis: A Handbook, 2nd edn. Sage Publications, London (2000)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)
Dunham, M.H.: Data Mining: Introductory and Advanced Topics. Prentice-Hall, Englewood Cliffs (2002)
Schwartz, M.E., Wood, D.C.M.: Discovering shared interests using graph analysis. Communications of ACM 36(8), 78–89 (1993)
Scott, J.: Social Network Analysis: A Handbook, 2nd edn. Sage Publications, London (2000)
Albert, R., Barabási, A.-L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47–97 (2002)
Newman, M.E.J.: The structure and function of complex networks. SIAM Review 45, 167–256 (2003)
Borgatti, S.P.: The network paradigm in organizational research: A review and typology. Journal of Management 29(6), 991–1013 (2003)
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)
Kleinberg, J.: The wireless epidemic. Nature 449, 287–288 (2007)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)
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)
Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: Proceedings CIKM 2003, November 3–8. ACM Press, New Orleans (2003)
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)
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)
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)
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)
Nong, Y. (ed.): The Handbook of Data Mining, vol. 689. Lawrence Erlbaum Associates, New Jersey (2003)
Fayyad, U.M.: Editorial. ACM SIGKDD Explorations 5(2), 1–3 (2003)
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)
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)
Schön, D.: The Reflective Practitioner. Basic Books, New York (1983)
Schön, D.: Educating The Reflective Practitioner. Jossey Bass, San Francisco (1991)
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)
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)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Simoff, S.J., Galloway, J. (2008). Visual Discovery of Network Patterns of Interaction between Attributes. In: Simoff, S.J., Böhlen, M.H., Mazeika, A. (eds) Visual Data Mining. Lecture Notes in Computer Science, vol 4404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71080-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-71080-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71079-0
Online ISBN: 978-3-540-71080-6
eBook Packages: Computer ScienceComputer Science (R0)