From numbers to information granules : a study of unsupervised learning and feature analysis / Andrzej Bargiela, Witold Pedrycz // W: Hybrid methods in pattern recognition / ed. by Horst Bunke, Abraham ... więcej

From numbers to information granules : a study of unsupervised learning and feature analysis / Andrzej Bargiela, Witold Pedrycz // W: Hybrid methods in pattern recognition / ed. by Horst Bunke, Abraham Kandel. – River Edge, N.J. : World Scientific, 2002. – (Series in Machine Perception and Artificial Intelligence ; 47). – S. 75-112 : rys. – Bibliogr. s. 112, Abstr. – doi: 10.1142/9789812778147_0004. – ISBN 978-981-02-4832-1

This chapter focuses on granular clustering: a way of finding structure in heterogeneous data and representing the data in the form of information granules. The main features of the proposed granular clustering ... więcej

This chapter focuses on granular clustering: a way of finding structure in heterogeneous data and representing the data in the form of information granules. The main features of the proposed granular clustering approach are: (a) a noninvasive exploration of data carried out under weak assumptions made as to the nature of the data, (b) transparency of the constructed information granules which assume the form of hyperboxes in the problem space. We introduce a compatibility measure that expresses a degree of “similarity” between two information granules and takes into account both a distance between the granules and their size. We show how to “grow” clusters through a process of merging existing data points that exhibit high values of the compatibility measure. The clustering algorithm is discussed along with a comprehensive validation mechanism for the resulting structures (collections of information granules). We formulate a problem of feature analysis in the setting of the information granules and introduce some quantitative measures describing each feature. Numerical experiments use two-dimensional synthetic data and the multivariable Boston housing data.