Interval-valued fuzzy set approach to fuzzy co-clustering for data classification

Abstract

Data clustering is aimed at discovering a structure in data. The revealed structure is usually represented in terms of prototypes and partition matrices. In some cases, the prototypes are simultaneously formed using data and features by running a co-clustering (bi-clustering) algorithm. Interval valued fuzzy clustering exhibits advantages when handling uncertainty. This study introduces a novel clustering technique by combining fuzzy co-clustering approach and interval-valued fuzzy sets in which two values of the fuzzifier of the fuzzy clustering algorithm are used to form the footprint of uncertainty (FOU). The study demonstrates the performance of the proposed method through a series of experiments completed for various datasets (including color segmentation, multi-spectral image classification, and document categorization). The experiments quantify the quality of results with the aid of validity indices and visual inspection. Some comparative analysis is also covered.