It has been claimed that tone language speakers use less F0 related cues in the production of verbal expressions of emotions. This is because F0 is used in the production of lexical tones. This study investigated this claim by examining how F0 and various other acoustic parameters are used in the production of verbal emotion expressions in Cantonese (tone language) compared to English (non-tone...

To analyze topics of a large number of web events, we proposed an event topic analysis approach by topic feature clustering and extended LDA (latent dirichlet allocation) model. The extended LDA model is dimension LDA (DLDA) which integrates topic probability of LDA model. We represent an event as a multi-dimensions vector and use DLDA model to select topic feature words in events. We aggregate...

This article examines several data mining approaches that perform short time series analysis. The basis of the methods is formed by clustering algorithms with or without modifications. The proposed methods implement short time series analysis when the numbers of the observations are not equal and the historical information is short. The inspected approaches are offered for solving complex tasks...

Author Diarization is a new task introduced in PAN’16, to identify portion(s) of text with in a document written by multiple authors. This paper presents, our proposed approach for author diarization task. Various types of stylistic features which include lexical features, used to uniquely identify an author. Furthermore, to find anomalous text with in a single document, ClustDist method used. ...

The k-means algorithm is the most popular nonparametric clustering method in use, but cannot generally be applied to data sets with missing observations. The usual practice with such data sets is to either impute the values under an assumption of a missing-at-random mechanism or to ignore the incomplete records, and then to use the desired clustering method. We develop an efficient version of t...

Clustering is used to generate groupings of data from a large dataset, with the intention of representing the behavior of a system as accurately as possible. In this sense, clustering is applied in this work to extract useful information from the electricity price time series. To be precise, two clustering techniques, K-means and Expectation Maximization, have been utilized for the analysis of ...

This paper takes an approach to clustering domestic electricity load profiles that has been successfully used with data from Portugal and applies it to UK data. Clustering techniques are applied and it is found that the preferred technique in the Portuguese work (a two stage process combining Self Organised Maps and Kmeans) is not appropriate for the UK data. The work shows that up to nine clus...

This paper presents the first participation of the University of Ottawa group in the Photo Retrieval task at Image CLEF 2008. Our system uses the following components: Lucene for text indexing and LIRE for image indexing. We experiment with several clustering methods in order to retrieve images from diverse clusters. The clustering methods are: k-means clustering, hierarchical clustering, and o...

participants and procedure The study was conducted using the paper-and-pencil method on 77 patients suffering from multiple sclerosis. The theory we applied was Hobfoll’s conservation of resources theory. We also analyzed the impact of personal and situational variables on the functioning of patients with different kinds of resources. results Cluster analysis was used to construct the profiles ...

The paper is about speeding-up the k-means clustering method which processes the data in a faster pace, but produces the same clustering result as the k-means method. We present a prototype based method for this where prototypes are derived using the leaders clustering method. Along with prototypes called leaders some additional information is also preserved which enables in deriving the k mean...