K-Means is a popular clustering algorithm to find the clustering easily by iteration. But the computational complexity of the traditional K-Means due to accessing the whole data in each cycle of iterative operations is too great to make it fit for very large data set. This paper presents a new clustering algorithm we have developed, Fast K-Means Clustering Algorithm based on Grid Data Reduction (GDR-FKM), by which clustering operations can be quickly performed on very large data set. Application of the algorithm to analysis of the data relativity in TT&C has demonstrated its celerity and accuracy.


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    Title :

    A Fast K-Means Clustering Algorithm Based on Grid Data Reduction


    Contributors:
    Li, Daqi (author) / Shen, Junyi (author) / Chen, Hongmin (author)


    Publication date :

    2008-03-01


    Size :

    2474769 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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