Constructing Stable Clustering Structure for Uncertain Data Set

Dmitri Viattchenin

doi:10.2478/v10198-011-0028-5

Abstract

The paper deals with the problem of constructing the stable clustering structure for the uncertain data set. The problem of explaining of stability of the clustering structure in automatic classification of objects for varying attributes values is formulated. The proposed method of the uncertain data clustering is based on heuristic algorithms of possibilistic clustering. Basic concepts of the heuristic approach to possibilistic clustering based on the concept of allotment among fuzzy clusters, a validity measure and techniques of the data preprocessing are considered. A method of constructing the set of values of most possible number of fuzzy clusters for the uncertain data is provided and a technique of constructing the stable clustering structure is proposed. An illustrative example of the proposed technique application to the oil data set is carried out. An analysis of the experimental results is given and preliminary conclusions are formulated.

Highlights

Some preliminary remarks are considered in the first subsection
The paper deals with the problem of constructing the stable clustering structure for the uncertain data set
The aim of the work is a detailed consideration of the method of the discovering the unique clustering structure, which corresponds to most natural allocation of objects among fuzzy clusters for the uncertain data set

Summary

Introduction

Some preliminary remarks are considered in the first subsection. Types of clustering structures are defined in the second subsection.1.1. Some preliminary remarks are considered in the first subsection. Types of clustering structures are defined in the second subsection. Fuzzy clustering methods have been applied effectively in image processing, data analysis and modeling. The most widespread approach in fuzzy clustering is the optimization approach and the optimization methods of fuzzy clustering are based on the concept of fuzzy c partition which is expressed as follows: measures of cluster validity associated with fuzzy c - partitions. The partition coefficient is described in [1] and compactness and separation index was defined in [4]. The compactness and separation index is most popular cluster validity criteria. That the index is appropriate for the ARCA-algorithm, because the ARCA-algorithm, though being a relational clustering algorithm, generates prototypes

Objectives

Results

Conclusion