A modified fuzzy min-max neural network for data clustering and its application to power quality monitoring

Manjeevan Seera, Chee Peng Lim, Chu Kiong Loo, Harapajan Singh

Research output: Contribution to journalArticleResearchpeer-review

36 Citations (Scopus)


When no prior knowledge is available, clustering is a useful technique for categorizing data into meaningful groups or clusters. In this paper, a modified fuzzy min-max (MFMM) clustering neural network is proposed. Its efficacy for tackling power quality monitoring tasks is demonstrated. A literature review on various clustering techniques is first presented. To evaluate the proposed MFMM model, a performance comparison study using benchmark data sets pertaining to clustering problems is conducted. The results obtained are comparable with those reported in the literature. Then, a real-world case study on power quality monitoring tasks is performed. The results are compared with those from the fuzzy c-means and k-means clustering methods. The experimental outcome positively indicates the potential of MFMM in undertaking data clustering tasks and its applicability to the power systems domain.

Original languageEnglish
Pages (from-to)19-29
Number of pages11
JournalApplied Soft Computing
Publication statusPublished - Mar 2015
Externally publishedYes


  • Benchmark study
  • Clustering
  • Fuzzy min-max neural network
  • Power quality monitoring

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