Please use this identifier to cite or link to this item:
Type: Congresso
Title: Participatory Learning Fuzzy Clustering For Interval-valued Data
Author: Maciel
Leandro; Ballini
Rosangela; Gomide
Fernando; Yager
Ronald R.
Abstract: 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.
Subject: Fuzzy Clustering
Participatory Learning
Interval Data
Editor: Springer Int Publishing AG
Rights: fechado
Identifier DOI: 10.1007/978-3-319-40596-4_57
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File SizeFormat 
000389515800057.pdf367.65 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.