Paper Info
Title
Clustering with Multidimensional Mixture Models: Analysis on World Development Indicators.
Abstract
Clustering is one of the core problems in machine learning. Many clustering algorithms aim to partition data along a single dimension. This approach may become inappropriate when data has higher dimension and is multifaceted. This paper introduces a class of mixture models with multiple dimensions called pouch latent tree models. We use them to perform cluster analysis on a data set consisting of 75 development indicators for 133 countries. We further propose a method that guides the selection of clustering variables due to the existence of multiple latent variables. The analysis results demonstrate that some interesting clusterings of countries can be obtained from mixture models with multiple dimensions but not those with single dimensions.
Year
DOI
Venue
2017
10.1007/978-3-319-59072-1_19
ADVANCES IN NEURAL NETWORKS, PT I
Keywords
Field
DocType
Multidimensional clustering,Pouch latent tree models,Mixture models,World development indicators,Clustering variables selection
Computer science,Latent variable,Artificial intelligence,Conceptual clustering,Cluster analysis,Partition (number theory),Machine learning,World Development Indicators,Mixture model,Multiple time dimensions
Conference
Volume
ISSN
Citations 
10261
0302-9743
2
PageRank 
References 
Authors
0.37
6
1
Name
Order
Citations
PageRank
Leonard K. M. Poon19410.96