K-means initial clustering center optimal algorithm based on estimating density and refining initial

The performance of K-means clustering algorithm strongly depends on the initial parameters. Based on the segmenting algorithm of density estimation and large scale data group segmenting algorithm of the initial value limitation, a new algorithm for initializing the cluster center is presented. The i...

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Bibliographic Details
Published in2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining pp. 603 - 606
Main Authors Hui Ai, Wei Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2012
Subjects
Accuracy
Classification algorithms
Clustering algorithms
clustering initialization
Costs
Distributed databases
estimating density algorithm
Estimation
Frequency conversion
k-means
Refining
refining initial algorithm
Time complexity
Time-frequency analysis
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ISBN9781467308762
1467308765

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Summary:The performance of K-means clustering algorithm strongly depends on the initial parameters. Based on the segmenting algorithm of density estimation and large scale data group segmenting algorithm of the initial value limitation, a new algorithm for initializing the cluster center is presented. The idea of segmenting base on density is combined with the idea of sampling and the new idea is presented. The accuracy of sampling is improved by averagely segmenting every dimension of the database. The speediness of the refining initial algorithm ensures the new algorithm has superiority on time. The experiment demonstrates that the new algorithm has superiority on time and accuracy with other algorithms.
ISBN:9781467308762
1467308765