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An efficient framework for obtaining the initial cluster centers.

B K Mishra1, Sachi Nandan Mohanty2, R R Baidyanath1

  • 1Silicon Institute of Technology, Bhubaneswar, Odisha, 751024, India.

Scientific Reports
|November 27, 2023
PubMed
Summary
This summary is machine-generated.

Improving data mining clustering, this study introduces new methods like farthest leap center selection (FLCS) to find better initial cluster centers than random selection, leading to more accurate subgroup discovery.

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Area of Science:

  • Data Mining and Machine Learning
  • Computational Statistics
  • Pattern Recognition

Background:

  • Clustering is vital for unsupervised pattern discovery in data mining.
  • The initial selection of cluster centers significantly impacts clustering quality.
  • Random center selection in K-Means can lead to suboptimal or erroneous clustering.

Purpose of the Study:

  • To propose qualitative approaches for selecting initial cluster centers.
  • To achieve well-separated and factual clusters.
  • To improve upon existing K-Means algorithms for enhanced clustering accuracy and efficiency.

Main Methods:

  • Development and analysis of novel K-Means variants: far efficient K-means (FEKM), modified center K-means (MCKM), modified FEKM using Quickhull (MFQ), and farthest leap center selection (FLCS).
  • Evaluation using clustering effectiveness metrics: Dunn's Index, Davies-Bouldin's Index, silhouette coefficient, Rand measure, and V-measure.
  • Comparative analysis of convergence speed and computational complexity against classical K-Means.

Main Results:

  • FEKM and FLCS consistently produce well-separated cluster centers.
  • FLCS demonstrates improved convergence speed compared to previous proposed methods.
  • All proposed methods, while slightly slower to converge than random K-Means, yield superior clustering outcomes.

Conclusions:

  • Qualitative approaches for initial cluster center selection significantly enhance clustering performance.
  • FLCS offers a balance between accurate center selection and faster convergence.
  • The proposed methods are effective for real-world datasets, outperforming random initialization in clustering quality.