The paper deals with the linguistic problem of fully automatic grouping of semantically related words. We discuss the measures of semantic relatedness of basic word forms and describe the treatment of collocations. Next we present the procedure of hierarchical clustering of a very large number of semantically related words and give examples of the resulting partitioning of data in the form of d...

In this work we analyse the relation between hierarchical distancebased clustering and the concepts that can be obtained from the hierarchy by generalisation. Many inconsistencies may arise, because the distance and the conceptual generalisation operator are usually incompatible. To overcome this, we propose an algorithm which integrates distance-based and conceptual clustering. The new dendrog...

This paper proposes an asymmetric-volatility based method for cluster analysis of stock returns. Using the information about the estimated parameters in the TARCH equation, we compute a distance matrix for the stock returns. Clusters are formed by looking to the hierarchical structure tree (or dendrogram) and the computed principal coordinates. We employ these techniques to investigate the simi...

A powerful framework for the representation, characterization and analysis of two-dimensional shapes, with special attention given to neurons, is presented. This framework is based on a recently reported approach to scale space skeletonization and respective reconstructions by using label propagation and the exact distance transform. This methodology allows a series of remarkable properties, in...

In hierarchical clustering, clusters are defined as branches of a cluster tree. The constant height branch cut, a commonly used method to identify branches of a cluster tree, is not ideal for cluster identification in complicated dendrograms. We describe a new dynamic branch cutting approach for detecting clusters in a cluster tree based on their shape. Compared to the constant height cutoff, o...

This paper introduces a new method of fuzzy semisupervised hierarchical clustering using fuzzy instance level constraints. It introduces the concepts of fuzzy must-link and fuzzy cannot-link constraints and use them to find the optimum α-cut of a dendrogram. This method is used to approach the problem of classifying scientific publications in web digital libraries. It is tested on real data fro...

DIVCLUS-T is a descendant hierarchical clustering algorithm based on a monothetic bipartitional approach allowing the dendrogram of the hierarchy to be read as a decision tree. We propose in this paper a new version of this method called C-DIVCLUS-T which is able to take contiguity constraints into account. We apply C-DIVCLUS-T to hydrological areas described by agricultural and environmental v...

Initialization of adaptive parameters in neural networks is of crucial importance to the speed of convergence of the learning procedure. Methods of initialization for the density networks are reviewed and two new methods, based on decision trees and dendrograms, presented. These two methods were applied in the Feature Space Mapping framework to artificial and real world datasets. Results show s...

Random amplified polymorphic DNA (RAPD) was used to study the genetic variations among strains from Fusarium sambucinum isolated from wheat in Upper Egypt. Using two different primer (V6 and M13), the used strains showed a high degree of similarities and a distinct RAPD patterns. The dendrogram that constructed based on M13 primers showed that, there is no correlation between isolation sources ...

PALI is a database of structure-based sequence alignments and phylogenetic relationships derived on the basis of three-dimensional structures of homologous proteins. This database enables grouping of pairs of homologous protein structures on the basis of their sequence identity calculated from the structure-based alignment and PALI also enables association of a new sequence to a family and auto...