This paper presents the TITech summarization system participating in TAC2009. Specifically, we discuss our results for the Update track. We propose a new method for creating summaries by ordering sentences. After a draft summary is obtained, we conduct agglomerative hierarchical clustering on the sentences of the draft summary based on sentence associativity. Then we use a probabilistic method ...

Link Clustering (LC) is a relatively new method for detecting overlapping communities in networks. The basic principle of LC is to derive a transform matrix whose elements are composed of the link similarity of neighbor links based on the Jaccard distance calculation; then it applies hierarchical clustering to the transform matrix and uses a measure of partition density on the resulting dendrog...

Incorporating spatial covariance into clustering has previously been considered for functional data to identify groups of functions which are similar across space. However, in the majority of situations that have been considered until now the most appropriate metric has been Euclidean distance. Directed networks present additional challenges in terms of estimating spatial covariance due to thei...

In this paper, we consider that agents judge the feasible alternatives through linguistic terms –when they are confident in their opinions– or linguistic expressions formed by several consecutive linguistic terms –when they hesitate. In this context, we propose an agglomerative hierarchical clustering process where the clusters of agents are generated by using a distance-based consensus measure.

Article history: Received 25 September 2007 Received in revised form 21 May 2008 Available online 22 July 2008 Communicated by L. Heutte

We present a short introduction to an hierarchical clustering method of high-dimensional data via localized

Cover song identification involves calculating pairwise similarities between a query audio track and a database of reference tracks. While most authors make exclusively use of chroma features, recent work tends to demonstrate that combining similarity estimators based on multiple audio features increases the performance. We improve this approach by using a hierarchical rank aggregation method f...

We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering...

We show that the Kullback-Leibler distance is a good measure of the statistical uncertainty of correlation matrices estimated by using a finite set of data. For correlation matrices of multivariate Gaussian variables we analytically determine the expected values of the Kullback-Leibler distance of a sample correlation matrix from a reference model and we show that the expected values are known ...