This paper examines the English particle placements of EFL learners’ writings in three East Asian countries (Chinese, Japan, and Korea). Three parts of the TOEFL11 corpus were chosen, and all the sentences with particles were extracted. The ICE-GB was chosen as a native speakers’ English. Then, eleven linguistic factors were manually encoded. The collected data were analyzed with R. Correlation...

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’).

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...

In this paper we explore the application of a novel data collection scheme for multi-sensory information to the question of whether different sensory domains tend to show similar relations between objects (along with some unique variance). Our analyses—hierarchical clustering, MDS mapping, and other comparisons between sensory domains— support the existence of common representational schemes fo...