Time-series mining in a psychological domain

Analysis of time-series became an inevitable tool in applied settings, such as stock market analysis, process and quality control, observation of natural phenomena, medical treatments, and in the behavioral science, such as psychological research. In this paper, we utilize a new kind of a tool set for time-series analysis (FAP, developed at Department of Mathematics and Informatics, University of Novi Sad) on behavioral data gained from a specific experimental lab system, a so called Socially Augmented Microworld with three human participants (developed by informatics and psychologists for Human Factors Research at Humboldt University Berlin). On the basis of these data (logfiles) we extracted three types of time-series and generated distance matrices using three kinds of time-series similarity measures. Finally, the clustering of generated distance matrices produced dendrograms which serve as the basis for a deeper analysis of human behavior. The outcome of this analysis is two-fold: (a) it allows to select the most suitable similarity measure for this domain of experimental research and (b) these results can serve as a basis for the development of artificial agents, which may in turn replace the human participants in the experiment.

[1]  Eamonn Keogh.  Exact Indexing of Dynamic Time Warping , 2002, VLDB.

[2]  Jignesh M. Patel,et al.  An efficient and accurate method for evaluating time series similarity , 2007, SIGMOD '07.

[3]  Dimitrios Gunopulos,et al.  Discovering similar multidimensional trajectories , 2002, Proceedings 18th International Conference on Data Engineering.

[4]  Lei Chen,et al.  Robust and fast similarity search for moving object trajectories , 2005, SIGMOD '05.

[5]  Clu-istos Foutsos,et al.  Fast subsequence matching in time-series databases , 1994, SIGMOD '94.

[6]  Eamonn J. Keogh.  A decade of progress in indexing and mining large time series databases , 2006, VLDB.

[7]  Ying Zhang,et al.  Time series analysis of a Web search engine transaction log , 2009, Inf. Process. Manag..

[8]  Mirjana Ivanovic,et al.  A Framework for Time-Series Analysis , 2010, AIMSA.

[9]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[10]  Hui Ding,et al.  Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..

[11]  Thanasis Hadzilacos,et al.  Advances in Spatial and Temporal Databases , 2015, Lecture Notes in Computer Science.

[12]  M. Ivanović,et al.  The Influence of Global Constraints on DTW and LCS Similarity Measures for Time-Series Databases , 2011 .

[13]  Ronen Feldman,et al.  The Data Mining and Knowledge Discovery Handbook , 2005 .

[14]  Dieter Pfoser,et al.  Advances in Spatial and Temporal Databases - 12th International Symposium, SSTD 2011, Minneapolis, MN, USA, August 24-26, 2011, Proceedings , 2011, SSTD.

[15]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[16]  Sascha Schubert,et al.  Time Series Data Mining with SAS ® Enterprise Miner ™ , 2011 .

[17]  Lei Chen,et al.  On The Marriage of Lp-norms and Edit Distance , 2004, VLDB.