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Pattern Analysis and Applications

Erschienen in:

01.05.2015 | Short Paper

A hybrid clustering algorithm based on missing attribute interval estimation for incomplete data

verfasst von: Li Zhang, Zhaohong Bing, Liyong Zhang

Erschienen in: Pattern Analysis and Applications | Ausgabe 2/2015

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Abstract

Partially missing data sets are a prevailing problem in clustering analysis. We propose a hybrid algorithm combining fuzzy clustering with particle swarm optimization (PSO) for incomplete data clustering, and missing attributes are represented as intervals. Furthermore, we develop a neighbor interval reconstruction (NIR) method based on pre-classification results that estimates the nearest-neighbor interval of missing attribute using the nearest-neighbor rule, which avoids endpoints of intervals determined by different species information, thereby improving the accuracy of missing attribute intervals and enhancing the robustness of missing attribute imputation. Then, the PSO and fuzzy c-means hybrid algorithm are used for clustering the interval-valued data set, and the global optimization ability of the PSO can improve the accuracy of clustering results compared with gradient-based optimization methods. The experimental results for several UCI data sets show the superiority of the proposed NIR hybrid algorithm.

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Titel: A hybrid clustering algorithm based on missing attribute interval estimation for incomplete data
verfasst von: Li Zhang
Zhaohong Bing
Liyong Zhang
Publikationsdatum: 01.05.2015
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 2/2015
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-014-0376-8

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