A dynamic understanding of customer behavior processes based on clustering and sequence mining

Abstract In this paper, a novel approach towards enabling the exploratory understanding of the dynamics inherent in the capture of customers’ data at different points in time is outlined. The proposed methodology combines state-of-art data mining clustering techniques with a tuned sequence mining method to discover prominent customer behavior trajectories in data bases, which — when combined — represent the “behavior process” as it is followed by particular groups of customers. The framework is applied to a real-life case of an event organizer; it is shown how behavior trajectories can help to explain consumer decisions and to improve business processes that are influenced by customer actions.

[1]  Marta E. Zorrilla,et al.  A service oriented architecture to provide data mining services for non-expert data miners , 2013, Decis. Support Syst..

[2]  Wil M. P. van der Aalst,et al.  Workflow mining: discovering process models from event logs , 2004, IEEE Transactions on Knowledge and Data Engineering.

[3]  B. Baesens,et al.  Financial Efficiency and Social Impact of Microfinance Institutions Using Self-Organizing Maps , 2013 .

[4]  Bart Baesens,et al.  A new SOM-based method for profile generation: Theory and an application in direct marketing , 2012, Eur. J. Oper. Res..

[5]  J. Coelho,et al.  Constructing Markov Models for Reliability Assessment with Self-Organizing Maps , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.

[6]  Alexander L. Wolf,et al.  Discovering models of software processes from event-based data , 1998, TSEM.

[7]  Bart Baesens,et al.  An Exploratory Approach for Understanding Customer Behavior Processes Based on Clustering and Sequence Mining , 2013, Business Process Management Workshops.

[8]  Heikki Mannila,et al.  Fast Discovery of Association Rules , 1996, Advances in Knowledge Discovery and Data Mining.

[9]  Bart Baesens,et al.  A new knowledge-based constrained clustering approach: Theory and application in direct marketing , 2014, Appl. Soft Comput..

[10]  Richard Weber,et al.  Improving Risk Grouping Rules for Prostate Cancer Patients Using Self-Organizing Maps , 2003, HIS.

[11]  Dimitrios Gunopulos,et al.  Mining Process Models from Workflow Logs , 1998, EDBT.

[12]  Kate Smith-Miles,et al.  Web page clustering using a self-organizing map of user navigation patterns , 2003, Decis. Support Syst..

[13]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Cesare Pozzi,et al.  Public Governance, Human Capital and Environmental Outcomes: an Analysis Based on Self-Organizing Maps , 2012 .

[15]  Bernardete Ribeiro,et al.  Clustering and visualization of bankruptcy trajectory using self-organizing map , 2013, Expert Syst. Appl..

[16]  André Skupin,et al.  Visualizing Demographic Trajectories with Self-Organizing Maps , 2005, GeoInformatica.

[17]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[18]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[19]  Richard Weber,et al.  Dynamic rough clustering and its applications , 2012, Appl. Soft Comput..

[20]  Wil M. P. van der Aalst,et al.  Decision Mining in ProM , 2006, Business Process Management.

[21]  Yishi Zhang,et al.  Hybrid retention strategy formulation in telecom based on k-means clustering analysis , 2011, 2011 International Conference on E-Business and E-Government (ICEE).

[22]  Bart Baesens,et al.  Country Corruption Analysis with Self Organizing Maps and Support Vector Machines , 2006, WISI.

[23]  I. Jolliffe.  Principal Component Analysis , 2002 .