Actas de congresos
Participatory Learning Fuzzy Clustering For Interval-valued Data
Information Processing And Management Of Uncertainty In Knowledge-based Systems, Ipmu 2016, Pt I. Springer Int Publishing Ag, v. 610, p. 687 - 698, 2016.
This paper suggests an interval participatory learning fuzzy clustering (iPL) method for partitioning interval-valued data. Participatory learning provides a paradigm for learning that emphasizes the pervasive role of what is already known or believed in the learning process. iPL clustering method uses interval arithmetic, and the Hausdorff distance to compute the (dis) similarity between intervals. Computational experiments are reported using synthetic interval data sets with linearly non-separable clusters of different shapes and sizes. Comparisons include traditional hard and fuzzy clustering techniques for interval-valued data as benchmarks in terms of corrected Rand (CR) index for comparing two partitions. The results suggest that the interval participatory learning fuzzy clustering algorithm is highly effective to cluster interval-valued data and has comparable performance than alternative hard and fuzzy interval-based approaches.61068769816th International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU)JUN 20-24, 2016Eindhoven, NETHERLANDS