Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Fast discovery of association rules
Advances in knowledge discovery and data mining
From Shell Logs to Shell Scripts
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A survey of kernel and spectral methods for clustering
Pattern Recognition
Sequence Data Mining (Advances in Database Systems)
Sequence Data Mining (Advances in Database Systems)
Multi-Dimensional Relational Sequence Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Journal of Artificial Intelligence Research
Probabilistic inductive logic programming
TildeCRF: conditional random fields for logical sequences
ECML'06 Proceedings of the 17th European conference on Machine Learning
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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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.