Meaningful distance for multivariate clustering

Alireza Naghizadeh, Dimitris Metaxas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we assume the decomposition of points with the mixture of Gaussian distributions in each dimension as an underlying assumption for feature formation of input data. The new guideline presents a unified approach to current basic assumptions and also provides us with an opportunity to solve an essential problem of low-level clustering algorithms. The issue is in the form of the curse of dimensionality which claims that multivariate clustering is meaningless for high dimensional data. To solve this problem, we propose a new type of vector norm (||.-||-c) and subsequently Clustering Distance (CD) which is a distance metric system that guarantees meaningfulness even in high dimensional data. The experiments on synthetic and non-synthetic datasets show the effectiveness of the proposed method compared to the current solutions.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1149-1154
Number of pages6
ISBN (Electronic)9781728113609
DOIs
StatePublished - Dec 2018
Externally publishedYes
Event2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018 - Las Vegas, United States
Duration: Dec 13 2018Dec 15 2018

Publication series

NameProceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018

Conference

Conference2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018
CountryUnited States
CityLas Vegas
Period12/13/1812/15/18

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Control and Optimization
  • Artificial Intelligence
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications
  • Modeling and Simulation

Keywords

  • Clustering
  • Curse of dimensionality
  • Distance metrics
  • High dimensional
  • K-mean
  • Meaningless

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  • Cite this

    Naghizadeh, A., & Metaxas, D. (2018). Meaningful distance for multivariate clustering. In Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018 (pp. 1149-1154). [8947779] (Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI46756.2018.00222