Clustering with multidimensional mixture models: Analysis on world development indicators

Research output: Chapter in Book/Report/Conference proceedingChapters

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. Copyright © 2017 Springer International Publishing AG.
Original languageEnglish
Title of host publicationAdvances in neural networks - ISNN 2017: 14th International Symposium, ISNN 2017, Sapporo, Hakodate, and Muroran, Hokkaido, Japan, June 21-26, 2017, Proceedings, Part I
EditorsFengyu CONG , Andrew LEUNG, Qinglai WEI
Place of PublicationCham
PublisherSpringer
Pages153-160
ISBN (Electronic)9783319590721
ISBN (Print)9783319590714
DOIs
Publication statusPublished - 2017

Citation

Poon, L. K. M. (2017). Clustering with multidimensional mixture models: Analysis on world development indicators. In F. Cong, A. Leung, & Q. Wei (Eds), Advances in neural networks - ISNN 2017: 14th International Symposium, ISNN 2017, Sapporo, Hakodate, and Muroran, Hokkaido, Japan, June 21-26, 2017, Proceedings, Part I (pp. 153-160). Cham: Springer.

Keywords

  • Multidimensional clustering
  • Pouch latent tree models
  • Mixture models
  • World development indicators
  • Clustering variables selection

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