In this paper we make two novel contributions to hierarchical clustering. First, we introduce an anomalous pattern initialisation method for hierarchical clustering algorithms, called A-Ward, capable of substantially reducing the time they take to converge. This method generates an initial partition with a sufficiently large number of clusters. This allows the cluster merging process to start f...

Previous works on automatic query clustering most generate a flat, un-nested partition of query terms. In this work, we are pursuing to organize query terms into a hierarchical structure and construct a query taxonomy in an automatic way. The proposed approach is designed based on a hierarchical agglomerative clustering algorithm to hierarchically group similar queries and generate the cluster ...

Cluster analysis is a data mining technique used to group based on the similarity of attributes object data. One problems that are often encountered in cluster with mixed categorical and numerical scale. The clustering stage for using ensemble ROCK (Robust Clustering links) method carried out by combining outputs from numeric scale Hierarchical Agglomerative method. best determined criteria rat...

This paper introduces a static Tree-based Multiple-Hop Distributed Hierarchical Agglomerative Clustering (TMH-DHAC) approach for wireless sensor networks (WSNs). The proposed TMH-DHAC is derived from the Hierarchical Agglomerative Clustering (HAC) and the distributed HAC (DHAC) methods. TMH-DHAC adopts an energy-aware cluster-head election policy to balance the energy consumption and workload a...

One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across many different fields ranging from computational biology to social sciences to computer vision in part because their output is easy to interpret. Unfortunately, it is well known, however, that many of the classic agglomerative clustering algorithms are not robust to...

Adaptive tree structured clustering (ATSC) is our proposed divisive hierarchical clustering method that recursively divides a data set into 2 subsets using self-organizing feature map (SOM). In each partition, the data set is quantized by SOM and the quantized data is divided using agglomerative hierarchical clustering. ATSC can divide data sets regardless of data size in feasible time. On the ...

Users prefer to navigate subjects from organized topics in an abundance resources than to list pages retrieved from search engines. We propose a framework to cluster frequent itemsets (sets of common words) into topics, produce a hierarchical list, and then generate topics sequence from a collection of documents. The framework will regenerate a next sequence when users click a topic. Consider b...

 In this paper a variant of the classical hierarchical cluster analysis is reported. This agglomerative (bottom-up) cluster technique is referred to as the Adaptive Mean-Linkage Algorithm. It can be interpreted as a linkage algorithm where the value of the threshold is conveniently up-dated at each interaction. The superiority of the adaptive clustering with respect to the average-linkage algo...