Clustering Objects Described by Juxtaposition of Binary Data Tables

Clustering Objects Described by Juxtaposition of Binary Data Tables

This paper seeks to develop an allocation of 0/1 data matrices to physical systems upon a Kullback-Leibler distance between probability distributions. The distributions are estimated from the contents of the data matrices. We discuss an ascending hierarchical classification method, a numerical example and mention an application with survey data concerning the level of development of the departm...

On Clustering Binary Data

Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. This paper studies the problem of clustering binary data. This is the case for market basket datasets where the transactions contain items and for document datasets where the documents contain “bag of words”. The contributio...

Clustering of Sparse Binary Data

Distance between Objects Described by Predicate Formulas

Functions defining a distance and a distinguish degree between objects described by predicate formulas are introduced. It is proved that the introduced function of distance satisfies all properties of a distance. The function of objects distinguish degree adequately reflects similarity of objects but does not define a distance because the triangle inequality is not fulfilled for it. The calcula...

Crossed Clustering method on Symbolic Data tables

In this paper we propose a crossed clustering algorithm in order to partition a set of symbolic objects in a predefined number of classes and to determine, in the same time, a structure (taxonomy) on the categories of the object descriptors. The procedure is an extension of the classical simultaneous clustering algorithms proposed on binary and contingency tables. Our approach is based on a dyn...