Clustering is a task of assigning a set of objects into groups called clusters. In data mining, hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one...

We present a novel method for improving hierarchical speaker clustering in the tasks of speaker diarization and speaker linking. In hierarchical clustering, a tree can be formed that demonstrates various levels of clustering. We propose a ratio that expresses the impact of each cluster on the formation of this tree and use this to rescale cluster scores. This provides score normalisation based ...

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...

MultiDendrograms is a Java-written application that computes agglomerative hierarchical clusterings of data. Starting from a distances (or weights) matrix, MultiDendrograms is able to calculate its dendrograms using the most common agglomerative hierarchical clustering methods. The application implements a variable-group algorithm that solves the non-uniqueness problem found in the standard pai...

Recommender system is the most important technology in e-commerce .It is used to suggest valuable products for the customer and improve their business intelligence. Collaborative filtering is a technique which is used to suggest information from similar kinds of users. Scalability is the biggest challenge in collaborative filtering recommender system. When more number of users is increasing in ...

In order to find the conditions which determine whether X-Ray hot gas in galaxy groups (intragroup gas; IGG) is heated externally or internally, we investigate the evolution of blast waves in galaxy groups growing on a hierarchical clustering scenario. We find that the blast waves driven by quasars are confined in groups and heat the IGG internally at z . 1. However, at z & 1, they expel the IG...

A new technique of topological multi-scale analysis is introduced. By performing a clustering recursively to build a hierarchy, and analyzing the co-scale and intra-scale similarities, an Iterated Function System can be extracted from any data set. The study of fractals shows that this method is efficient to extract self-similarities, and can find elegant solutions the inverse problem of buildi...

Hierarchical clustering is a common method used to determine clusters of similar data points in multidimensional spaces. O(n 2) algorithms are known for this problem 3, 4, 10, 18]. This paper reviews important results for sequential algorithms and describes previous work on parallel algorithms for hierarchical clustering. Parallel algorithms to perform hierarchical clustering using several dist...

The mean intracluster gas fraction of X–ray clusters within their hydrostatic regions is derived from recent observational compilations of David, Jones & Forman and White & Fabian. At radii encompassing a mean density 500 times the critical value, the individual sample bi-weight means are moderately (2.4σ) discrepant; revising binding masses with a virial relation calibrated by numerical simula...

Abstract: Distance or similarity measures are of fundamental importance to pattern classification, clustering, and information retrieval problems. Various distance/similarity measures that are applicable to compare two nominal type histograms are reviewed and categorized in both syntactic and semantic relationships. A correlation coefficient and a hierarchical clustering technique are adopted t...