Missing Data Clustering Based on Incomplete Information System

Yong Lin Leng

doi:10.4028/www.scientific.net/AMM.687-691.1500

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Missing Data Clustering Based on Incomplete...

Missing Data Clustering Based on Incomplete Information System

Abstract:

With the development of information technology and data collection capabilities improve, the amount of data accumulated increase, missing data problems are more and more obvious. Traditional clustering methods can not cluster data set which contained missing data directly. In this paper, we proposed a novel missing data measurement method based on the incomplete information system theory and designed the similarity measure criterion for the discrete and successive of attributes separately. The experiment uses K-means clustering to test algorithm accuracy from different missing data rate and different amount of data two aspects, results demonstrate that the method can cluster missing data set efficiently and accurately.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

1500-1503

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.1500

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Online since:

November 2014

Authors:

Yong Lin Leng*

Keywords:

Clustering, Incomplete Information System, Missing Data

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* - Corresponding Author

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