Multidimensional Balance-Based Cluster Boundary Detection for High-Dimensional Data

Xiaofeng Cao, Baozhi Qiu, Xiangli Li, Zenglin Shi, Guandong Xu*, Jianliang XU

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The balance of neighborhood space around a central point is an important concept in cluster analysis. It can be used to effectively detect cluster boundary objects. The existing neighborhood analysis methods focus on the distribution of data, i.e., analyzing the characteristic of the neighborhood space from a single perspective, and could not obtain rich data characteristics. In this paper, we analyze the high-dimensional neighborhood space from multiple perspectives. By simulating each dimension of a data point's k nearest neighbors space (k NNs) as a lever, we apply the lever principle to compute the balance fulcrum of each dimension after proving its inevitability and uniqueness. Then, we model the distance between the projected coordinate of the data point and the balance fulcrum on each dimension and construct the DHBlan coefficient to measure the balance of the neighborhood space. Based on this theoretical model, we propose a simple yet effective cluster boundary detection algorithm called Lever. Experiments on both low- and high-dimensional data sets validate the effectiveness and efficiency of our proposed algorithm.

Original languageEnglish
Article number8517163
Pages (from-to)1867-1880
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume30
Issue number6
DOIs
Publication statusPublished - Jun 2019

Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • Balance principle
  • cluster boundary
  • high-dimensional space
  • unlimited lever

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