Latent class models for cognitive diagnosis often begin with specification of a matrix that indicates which attributes or skills are needed for each item. Then by imposing restrictions that take this into account, along with a theory governing how subjects interact with items, parametric formulations of item response functions are derived and fitted. Cluster analysis provides an alternative app...

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...

 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...

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 ...

This paper presents the results of an experimental study of some common document clustering techniques. In particular, we compare the two main approaches to document clustering, agglomerative hierarchical clustering and K-means. (For K-means we used a “standard” K-means algorithm and a variant of K-means, “bisecting” K-means.) Hierarchical clustering is often portrayed as the better quality clu...

The ability to visualize documents into clusters is very essential. The best data summarization technique could be used to summarize data but a poor representation or visualization of it will be totally misleading. As proposed in many researches, clustering techniques are applied and the results are produced when documents are grouped in clusters. However, in some cases, user may want to know t...

The proper representation of emotion is critical to automatic classification systems. In previous research, we demonstrated that emotion profile (EP) based representations are effective for this task. In EP-based representations, emotions are expressed in terms of underlying affective components from the subset of anger, happiness, neutrality, and sadness. The current study explores cluster pro...

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...

In this paper, we present a framework for unsupervised domain adaptation of PLDA based i-vector speaker recognition systems. Given an existing out-of-domain PLDA system, we use it to cluster unlabeled in-domain data, and then use this data to adapt the parameters of the PLDA system. We explore two versions of agglomerative hierarchical clustering that use the PLDA system. We also study two auto...

This paper proposes a novel cluster modeling method for intercluster distance measurement within the framework of agglomerative hierarchical speaker clustering, namely, incremental Gaussian mixture cluster modeling. This method uses a single Gaussian distribution to model each initial cluster, but represents any newly merged cluster using a distribution whose pdf is the weighted sum of the pdf’...