Ant based clustering of time series discrete data --- a rough set approach

Authors:
Krzysztof Pancerz;Arkadiusz Lewicki;Ryszard Tadeusiewicz
Affiliations:
University of Information Technology and Management in Rzeszów, Poland;University of Information Technology and Management in Rzeszów, Poland;AGH University of Science and Technology, Kraków, Poland
Venue:
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Year:
2011

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Abstract

This paper focuses on clustering of time series discrete data. In time series data, each instance represents a different time step and the attributes give values associated with that time. In the presented approach, we consider discrete data, i.e., the set of values appearing in a time series is finite. For ant-based clustering, we use the algorithm based on the versions proposed by J. Deneubourg, E. Lumer and B. Faieta. As a similarity measure, the so-called consistency measure defined in terms of multistage decision transition systems is proposed. A decision on raising or dropping a given episode by the ant is made on the basis of a degree of consistency of that episode with the knowledge extracted from the neighboring episodes.