Showing papers on "Fuzzy clustering published in 1976"

A Graph-Theoretic Approach to Nonparametric Cluster Analysis

Abstract: Nonparametric clustering algorithms, including mode-seeking, valley-seeking, and unimodal set algorithms, are capable of identifying generally shaped clusters of points in metric spaces. Most mode and valley-seeking algorithms, however, are iterative and the clusters obtained are dependent on the starting classification and the assumed number of clusters. In this paper, we present a noniterative, graph-theoretic approach to nonparametric cluster analysis. The resulting algorithm is governed by a single-scalar parameter, requires no starting classification, and is capable of determining the number of clusters. The resulting clusters are unimodal sets.

3. Clustering Analysis

Abstract: Cluster analysis is one of the Pattern Recognition techniques and should be ap­ preciated as such. It may be characterized by the use of resemblance or dis­ semblance measures between the objects to be identified. An evaluation of the significations of such measures is made, examples are given. After presenting a general model of clustering techniques, the general prop­ erties of a cluster, of a clustering operator and of a clustering model are examined. The goal of a clustering analysis is usually to obtain a symbolic description of the problem and from this an identification procedure. The main clustering algorithms are presented according to the symbolic descriptions: hierarchies, minimum spanning trees, partitions and their representations. A special attention is given to the dynamic cluster method, which handles adaptively a partition in clusters and a set of symbolic representations of the clusters. Examples are given of this method when the symbolic representation is either a kernel, a probability law or a linear manifold. Results are given of the dynamic cluster algorithm when the distance measures may also be modified among a class of distance. Some general conclusions and proposal of research are finally given.

An approach to associative retrieval through the theory of fuzzy sets

Abstract: The theory of fuzzy sets is used to provide a rigorous formulation of the problem of associative retrieval. This formulation suggests the idea of using fuzzy clustering to organize data for retrieval.

Fuzzy Models for Social Processes

Abstract: A general account of some techniques applied in the study of complex systems is given, suggesting their possible extensions in the field of social sciences. The theory of fuzzy sets is introduced as well-suited for modelling the social group. Fuzzy clustering, fuzzy categories and fuzzy systems are presented as possible models of social phenomena.

Application of Automatic Clustering to Emitter Identification

Abstract: : The goal of clustering is the partitioning of a given set of objects into subsets called clusters in such a way that the objects in a cluster are similar to one another and that objects in different clusters are dissimilar Clustering may help in getting a more or less direct understanding of the relationships among the objects, and it may be useful as a first step in pattern recognition Some possible applications are automatic phoneme recognition, data base management systems, personnel classification, detection of errors in files and computer security Several clustering methods were applied to data sets of practical importance Automatic pattern recognition using the k nearest neighbors was applied An efficient method for selecting a good subset from the full of 44 features was tried In all cases, the results were good (Author)