Commentary: Classification and cluster analysis

Cluster Analysis and Classification of Named Entities

This paper presents a statistics-based and language independent unsupervised approach for clustering possible named entities. We describe and motivate the features and statistical filters used by our clustering process. Using the Model-Based Clustering Analysis software we obtained different clusters of named entities. The method was applied to Bulgarian and English. For some clusters, precisio...

Cluster analysis and classification of heart sounds

Acoustic heart signals, generated by the mechanical processes of the cardiac cycle, carry significant information about the underlying functioning of the cardiovascular system.We describe a computational analysis framework for identifying distinct morphologies of heart sounds and classifying them into physiological states. The analysis framework is based on hierarchical clustering, compact data...

Recent Advances in Mathematical Programming for Classification and Cluster Analysis

This chapter is focused on recent advances in mathematical programming methodologies in data mining research, which is a rapidly emerging interdisciplinary research area. The main focus of this review chapter lies on classification (supervised learning) and clustering (unsupervised learning), which are among the most studied data mining tasks. We give a thorough discussion on the mathematical m...

Formulations Of Multidimensional Scaling For Cluster Analysis And Classification

Morphological boundary detection for cluster analysis and HEP data classification

This paper gives a new mode boundary approach for cluster analysis, based on mathematical concepts. It consists of a fast Parzen estimation of the underlying p.d.f. and a smoothing using numerical morphological transformation. The performance of this approach, with respect to a defined classification rule, is demonstrated on SM Higgs boson identification using generated LHC events.