This project explores unsupervised clustering of vehicle silhouettes using image-derived shape features. The objective is to automatically discover meaningful groupings in the data, guided by ...

The Fundamental Clustering Problems Suite (FCPS) summaries 54 state-of-the-art clustering algorithms, common cluster challenges and estimations of the number of clusters as well as the testing for ...

In this paper, time series are used as the analyzed values and the focus is on their data mining methods. This detailed evaluation highlights the clustering algorithms in time series data mining. The ...

Various clustering identification methods have been developed in the context of the statistical data analysis [35, 36] and several have been used to study the spatio-temporal patterns of natural and ...

As an unsupervised learning method, spatial clustering has emerged to be one of the most important techniques in the field of agriculture for soil data analysis. Soil data analysis is usually related ...

We have performed only clustering analysis on arterial hypertension, diabetes mellitus, obesity, and smoker. Because other common heart diseases such as myocardial infarction, cardiomyopathy, ...

ABSTRACT: This paper provides an intuitive introduction to cluster analysis. Our targeting audience are both scholars and students in Political Science. Methodologically, we use basic simulation to ...

Cluster analysis involves computational procedures, of which purpose is to reduce a set of data on several relatively homogenous groups-clusters, while the condition of reduction is maximal and ...