Semi-supervised approaches have proven to be effective in clustering tasks. They allow user input, thus improving the quality of the clustering obtained, while maintaining a controllable level of user intervention. Despite being an important class of algorithms, hierarchical clustering has been little explored in semisupervised solutions. In this report, we address the problem of semi-supervise...

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...

Clustering is an automatic learning technique aimed at grouping a set of objects into subsets or clusters. Objects in the same cluster should be as similar as possible, whereas objects in one cluster should be as dissimilar as possible from objects in the other clusters. Document clustering has become an increasingly important task in analysing huge documents. The challenging aspect to analyse ...

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’...

Corresponding Author Tb Ai Munandar Department of Informatics Engineering, Faculty of Information Technology, Universitas Serang Raya, Banten, Indonesia Email: [email protected] Abstract: The identification of regional development gaps is an effort to see how far the development conducted in every District in a Province. By seeing the gaps occurred, it is expected that the Policymakers are...

CLOPE (Clustering with sLOPE) is a simple and fast histogram-based clustering algorithm for categorical data. However, given the same data set with the same input parameter, the clustering results by this algorithm would possibly be different if the transactions are input in a different sequence. In this paper, a hierarchical clustering framework is proposed as an extension of CLOPE to generate...

INTRODUCTION The aim of this study was to identify subsets of patients with fibromyalgia with similar symptom profiles using the Outcome Measures in Rheumatology (OMERACT) core symptom domains. METHODS Female patients with a diagnosis of fibromyalgia and currently meeting fibromyalgia research survey criteria completed the Brief Pain Inventory, the 30-item Profile of Mood States, the Medical ...

Agglomerative hierarchical cluster analysis was used to group similar spectra from a large database of samples. Based on angles between reflectance vectors of members of a cluster, a reflectance vector was selected as representative of that cluster. Representative samples were grouped together and stored as new calibration targets. Simulated wide-band imaging with glass filters was performed us...