Relational Sequence Clustering for Aggregating Similar Agents

Many clustering methods are based on flat descriptions, while data regarding real-world domains include heterogeneous objects related to each other in multiple ways. For instance, in the field of Multi-Agent System, multiple agents interact with the environment and with other agents. In this case, in order to act effectively an agent should be able to recognise the behaviours adopted by other agents. Actions taken by an agent are sequential, and thus its behaviour can be expressed as a sequence of actions. Inferring knowledge about competing and/or companion agents by observing their actions is very beneficial to construct a behavioural model of the agent population. In this paper we propose a clustering method for relational sequences able to aggregate companion agent behaviours. The algorithm has been tested on a real world dataset proving its validity.

[1]  Luc De Raedt,et al.  Probabilistic Inductive Logic Programming , 2004, Probabilistic Inductive Logic Programming.

[2]  Francesco Masulli,et al.  A survey of kernel and spectral methods for clustering , 2008, Pattern Recognit..

[3]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[4]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

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

[6]  Luc De Raedt,et al.  Relational Sequence Learning , 2008, Probabilistic Inductive Logic Programming.

[7]  Jeffrey D. Uuman.  Principles of database and knowledge- base systems , 1989 .

[8]  Heikki Mannila,et al.  Verkamo: Fast Discovery of Association Rules , 1996, KDD 1996.

[9]  Saul Greenberg,et al.  USING UNIX: COLLECTED TRACES OF 168 USERS , 1988 .

[10]  Luc De Raedt,et al.  Inductive Logic Programming: Theory and Methods , 1994, J. Log. Program..

[11]  Stefano Ferilli,et al.  Multi-Dimensional Relational Sequence Mining , 2008, Fundam. Informaticae.

[12]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[13]  Kristian Kersting,et al.  TildeCRF: Conditional Random Fields for Logical Sequences , 2006, ECML.

[14]  Jiong Yang,et al.  CLUSEQ: efficient and effective sequence clustering , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).

[15]  L. De Raedt,et al.  Logical Hidden Markov Models , 2011, J. Artif. Intell. Res..

[16]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[17]  Jian Pei,et al.  Sequence Data Mining (Advances in Database Systems) , 2007 .

[18]  Neil D. Lawrence,et al.  Missing Data in Kernel PCA , 2006, ECML.

[19]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[20]  David G. Stork,et al.  Pattern Classification , 1973 .

[21]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[22]  Hendrik Blockeel,et al.  From Shell Logs to Shell Scripts , 2001, ILP.

[23]  Jeffrey D. Ullman,et al.  Principles of Database and Knowledge-Base Systems, Volume II , 1988, Principles of computer science series.

[24]  Marc Teboulle,et al.  Grouping Multidimensional Data - Recent Advances in Clustering , 2006 .