An improved K-means clustering algorithm

Jian Zhu; Hanshi Wang

doi:10.1109/icime.2010.5478087

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JOURNAL ARTICLE

An improved K-means clustering algorithm

Jian Zhu Hanshi Wang

Year: 2010 Pages: 190-192

DOI: 10.1109/icime.2010.5478087

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Abstract

That traditional K-mean algorithm is a widely used clustering algorithm, with a wide application. In light of the disadvantage of K-mean algorithm, improvement is made to the traditional K-mean algorithm, a k value learning algorithm is proposed. Using genetic algorithm to optimize the K value, and improve clustering performance.

Keywords:

Cluster analysis Canopy clustering algorithm Computer science Algorithm CURE data clustering algorithm Population-based incremental learning k-means clustering Algorithm design Value (mathematics) Genetic algorithm Correlation clustering Artificial intelligence Machine learning

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Financial Risk and Volatility Modeling

Social Sciences → Economics, Econometrics and Finance → Finance

Remote Sensing and Land Use

Physical Sciences → Earth and Planetary Sciences → Atmospheric Science

Data Management and Algorithms

Physical Sciences → Computer Science → Signal Processing

An improved K-means clustering algorithm

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