Research in cluster analysis has resulted in a large number of algorithms and similarity measurements for clustering scienti c data. Machine learning researchers have published a number of methods for conceptual clustering, in which observations are grouped into clusters which have \good" descriptions in some language. We investigate the general properties which similarity metrics, objective fu...

This paper describes the findings from evaluating the performance of agglomerative hierarchical cluster methods for determining seasonal factor groups. Seasonal factor groups are usually determined by traditional cluster analysis based on various similarity measures. Agglomerative hierarchical methods merge telemetry traffic monitoring sites (TTMSs) into groups according to their similarities. ...

This paper presents a new approach to agglomerative hierarchical clustering. Classical hierarchical clustering algorithms are based on metrics which only consider the absolute distance between two clusters, merging the pair of clusters with highest absolute similarity. We propose a relative dissimilarity measure, which considers not only the distance between a pair of clusters, but also how dis...

This paper introduces a hybrid hierarchical clustering method, which is a novel method for speeding up agglomerative hierarchical clustering by seeding the algorithm with clusters obtained from K-means clustering. This work describes a benchmark study comparing the performance of hybrid hierarchical clustering to that of conventional hierarchical clustering. The two clustering methods are compa...

We are interested in finding clusters (“communities”) in networks of linked data, such as citation networks or web pages. Hierarchical clustering for networks is reviewed and an algorithmic improvement that leads to a significant performance increase is introduced. Our main focus is on the development of partitioning clustering algorithms that can deal with data represented only by link informa...

In this paper, we present a hybrid clustering method that combines the divisive hierarchical clustering with the agglomerative hierarchical clustering. We used the bisect K-means divisive clustering algorithm in our method. First, we cluster the document collection using bisect K-means clustering algorithm with K’ > K as the total number of clusters. Second, we calculate the centroids of K’ clu...

DIVCLUS-T is a divisive hierarchical clustering algorithm based on a monothetic bipartitional approach allowing the dendrogram of the hierarchy to be read as a decision tree. It is designed for either numerical or categorical data. Like the Ward agglomerative hierarchical clustering algorithm and the k-means partitioning algorithm, it is based on the minimization of the inertia criterion. Howev...

Cluster analysis refers to a class of data reduction methods used for sorting cases, observations, or variables of a given dataset into homogeneous groups that differ from each other. The present paper focuses on hierarchical agglomerative cluster analysis, a statistical technique where groups are sequentially created by systematically merging similar clusters together, as dictated by the dista...