This work presents a clustering method which can be applied to relational knowledge bases. Namely, it can be used to discover interesting groupings of semantically annotated resources in a wide range of concept languages. The method exploits a novel dissimilarity measure that is based on the resource semantics w.r.t. a number of dimensions corresponding to a committee of features, represented b...

This paper describes a graph visualization methodology based on hierarchical maximal modularity clustering, with interactive and significant coarsening and refining possibilities. An application of this method to HIV epidemic analysis in Cuba is outlined.

Recommender systems have been used in education to assist users in the discovery of learning resources. Unlike product-oriented recommender systems, the goals and behavior of users in education are influenced by their context; such influence may be stronger in formal scenarios such as primary and secondary education since context is highly regulated. Intuitively, we could assume that a biology ...

When we humans are asked whether or not the emotions in two speech samples are in the same category, the judgment depends on the size of the target category. Hierarchical clustering is a suitable technique for simulating such perceptions by humans of relative similarities of the emotions in speech. For better reflection of subjective similarities in clustering results, we have devised a method ...

Hierarchical clustering is one of the most powerful solutions to the problem of clustering, on the grounds that it performs a multi scale organization of the data. In recent years, research on hierarchical clustering methods has attracted considerable interest due to the demanding modern application domains. We present a novel divisive hierarchical clustering framework called Hierarchical Stoch...

This paper investigates the applicability of distributed clustering technique, called RACHET [1], to organize large sets of distributed text data. Although the authors of RACHET claim that the algorithm generates quality clusters for massive and high dimensional data set, the algorithm was not yet evaluated on a well known academic data set. This paper presents performance analysis of the algor...

In this work we consider hierarchical clustering algorithms, such as UPGMA, which follow the closest-pair joining scheme. We survey optimal O(n)-time implementations of such algorithms which use a ‘locally closest’ joining scheme, and specify conditions under which this relaxed joining scheme is equivalent to the original one (i.e. ‘globally closest’).

We describe the Hierarchical Classifier (HC), which is a hybrid architecture [1] built with the help of supervised training and unsu-pervised problem clustering. We prove a theorem giving the estimationˆR of HC risk. The proof works because of an improved way of computing cluster weights, introduced in this paper. Experiments show thatˆR is correlated with HC real error. This allows us to usê R...

Contemporary datasets are becoming increasingly larger and more complex, while techniques to analyse them are becoming more and more inadequate. Thus, new methods are needed to handle these new types of data. This study introduces methods to cluster histogram-valued data. However, histogram-valued data are difficult to handle computationally because observations typically have a different numbe...

This study investigates the influence of aspects of home and preschool environments upon literacy and numeracy achievement at school entry and at the end of the third year of school. Individuals with unexpected performance pathways (by forming demographically adjusted groups: overachieving, average, and underachieving) were identified in order to explore the effects of the Home Learning Environ...