Constrained clustering received a lot of attention in the last years. However, the widely used pairwise constraints are not generally applicable for hierarchical clustering, where the goal is to derive a cluster hierarchy instead of a flat partition. Therefore, we propose for the hierarchical setting—based on the ideas of pairwise constraints—the use of must-link-before (MLB) constraints. In th...

The taxonomy of N2-fixing bacteria belonging to the genus Bradyrhizobium is still poorly refined. This paper presents an application of a method aiming the identification of possible new clusters within a Brazilian collection of 119 Bradyrhizobium strains showing phenotypic characteristics of B. japonicum and B. elkanii. The stability has been studied as a function of the number of restriction ...

Hierarchical clustering of speakers by their pronunciation patterns could be a useful technique for the discovery of accents and the relationships between accents and sociological variables. However it is first necessary to ensure that the clustering is not influenced by the physical characteristics of the speakers. In this study a number of approaches to agglomerative hierarchical clustering o...

User groups identification is an important task in intelligent personalized information service. In this paper we proposed an intelligent information service model based on hierarchical clustering algorithms. There are two main works in this article: firstly, a vector models which can represent users' interests, preferences and emotional information is introduced. Secondly, a user groups cluste...

Abstract— Clustering is a powerful technique of data mining for extracting useful information from a set of data and classifies the data into several clusters based on similarity of the pattern. This paper presents the quality estimation for students’ projects data based on hierarchical clustering and fuzzy clustering using Min-Max method. From the experimental results it is seen the fuzzy clus...

This article describes a novel way of representing large databases of shapes. We propose a hierarchical clustering of a set of Fourier-transformed contours. The clustering analysis is density-based and is performed using topographic maps. We have tested the approach on a database of extracted contours of marine animals, generated by Mokhtarian and co-workers. The resulting clusters group contou...

Distributed-system observation tools require an efficient data structure to store and query the partial-order of execution. Such data structures typically use vector timestamps to efficiently answer precedence queries. Many current vector-timestamp algorithms either have a poor time/space complexity tradeoff or are static. This limits the scalability of such observation tools. One algorithm, ce...

A class-conditional hierarchical clustering framework has been used to generalize and improve previously proposed condensing schemes to obtain multiple prototype classifiers. The proposed method conveniently uses geometric properties and clusters to efficiently obtain reduced sets of prototypes that accurately represent the data while significantly keeping its discriminating power. The benefits...

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...

Conventional clustering means classifying the given data objects as exclusive subsets (clusters).That means we can discriminate clearly whether an object belongs to a cluster or not. However such a partition is insufficient to represent many real situations. Therefore a fuzzy clustering method is offered to construct clusters with uncertain boundaries and allows that one object belongs to overl...