Clustering of modal-valued symbolic data

Symbolic Clustering with Interval-Valued Data

Hierarchical clustering of modal ordinal symbolic data objects

The problem of analyzing the dispersion of a set of objects described by ordinal modal symbolic data is addressed in order to obtain homogeneous groups, which are evaluated by a consensus measure. Based on a generalized φ function a consensus measure for objects and for sets of objects described by modal ordinal data is defined. A variability measure for sets of subsets of objects based in the ...

Dissimilarity measures for histogram-valued data and divisive clustering of symbolic objects

Contemporary datasets are becoming increasingly larger and more complex, while techniques to analyse them are becoming more and more inadequate. Thus, new methods are needed to handle these new types of data. This study introduces methods to cluster histogram-valued data. However, histogram-valued data are difficult to handle computationally because observations typically have a different numbe...

The Comparison of Fuzzy Clustering Methods for Symbolic Interval-valued Data

Interval-valued data can find their practical applications in such situations as recording monthlyinterval temperatures at meteorological stations, daily interval stock prices, etc. The primary objectiveof the presented paper is to compare three different methods of fuzzy clustering for interval-valuedsymbolic data, i.e.: fuzzy c-means clustering, adaptive fuzzy c-means clustering a...

Fuzzy clustering for symbolic data

Most of the techniques used in the literature in clustering symbolic data are based on the hierarchical methodology, which utilizes the concept of agglomerative or divisive methods as the core of the algorithm. The main contribution of this paper is to show how to apply the concept of fuzziness on a data set of symbolic objects and how to use this concept in formulating the clustering problem o...