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

Zhang, Li; Bing, Zhaohong; Zhang, Liyong

doi:10.1007/s10044-014-0376-8

Cited by 25 publications

(5 citation statements)

References 26 publications

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“…To simplify notations, in the following, let = ( − + + )/2 and = ( + − − )/2 for any ∈ 0 and = 0 for any ∈ 1 . For details on how to construct these intervals for missing values, see Li et al [20] and Zhang et al [23].…”

Section: Robust K-median and K-means Clustering For Incompletementioning

confidence: 99%

“…Wang et al [22] use an improved backpropagation (BP) neural network to estimate the interval data for missing values. Zhang et al [23] propose an improved interval construction method based on preclassification results and use the particle swarm optimization to search for the optimal clustering. Zhang et al [8] represent the missing values by probabilistic information granules and design an efficient trilevel alternating optimization method to find both the optimal clustering results and the optimal missing values simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the interval data representation, we present robust K-median and K-means clustering algorithms. Different from the existing algorithms, which use either the interval distance function or optimal imputation [20,21,23], we reformulate the clustering problem as a minimax robust optimization problem based on interval data.…”

Section: Introductionmentioning

confidence: 99%

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Robust K-Median and K-Means Clustering Algorithms for Incomplete Data

Song

Zhang

et al. 2016

Mathematical Problems in Engineering

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Incomplete data with missing feature values are prevalent in clustering problems. Traditional clustering methods first estimate the missing values by imputation and then apply the classical clustering algorithms for complete data, such as K-median and K-means. However, in practice, it is often hard to obtain accurate estimation of the missing values, which deteriorates the performance of clustering. To enhance the robustness of clustering algorithms, this paper represents the missing values by interval data and introduces the concept of robust cluster objective function. A minimax robust optimization (RO) formulation is presented to provide clustering results, which are insensitive to estimation errors. To solve the proposed RO problem, we propose robust K-median and K-means clustering algorithms with low time and space complexity. Comparisons and analysis of experimental results on both artificially generated and real-world incomplete data sets validate the robustness and effectiveness of the proposed algorithms.

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Section: Robust K-median and K-means Clustering For Incompletementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust K-Median and K-Means Clustering Algorithms for Incomplete Data

Song

Zhang

et al. 2016

Mathematical Problems in Engineering

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show abstract

“…If particles relative to the global best position have not improved for consecutive A times, then the particle swarm is considered as gathering to a local optimum location and positions of the entire particle swarm are mutated with a certain mutation probability

1 / (1 + t^{0.5})

, where t stands for the number of iterations. The position of the particle is changed as follows:

p_{j} (t) = r \cdot (max - min) + min,

where r is a random number uniformly distributed between 0 and 1, and the “max” and the “min” are the upper bound and the lower bound of the search space, respectively …”

Section: Hybrid Optimization For Incomplete Data Clusteringmentioning

confidence: 99%

“…where r is a random number uniformly distributed between 0 and 1, and the "max" and the "min" are the upper bound and the lower bound of the search space, respectively. 41…”

Section: Mutation Strategymentioning

confidence: 99%

A Global Clustering Approach Using Hybrid Optimization for Incomplete Data Based on Interval Reconstruction of Missing Value

Zhang

Liu

et al. 2015

Int. J. Intell. Syst.

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Incomplete data clustering is often encountered in practice. Here the treatment of missing attribute value and the optimization procedure of clustering are the important factors impacting the clustering performance. In this study, a missing attribute value becomes an information granule and is represented as a certain interval. To avoid intervals determined by different cluster information, we propose a congeneric nearest‐neighbor rule‐based architecture of the preclassification result, which can improve the effectiveness of estimation of missing attribute interval. Furthermore, a global fuzzy clustering approach using particle swarm optimization assisted by the Fuzzy C‐Means is proposed. A novel encoding scheme where particles are composed of the cluster prototypes and the missing attribute values is considered in the optimization procedure. The proposed approach improves the accuracy of clustering results, moreover, the missing attribute imputation can be implemented at the same time. The experimental results of several UCI data sets show the efficiency of the proposed approach.

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A Clustering Algorithm Based on Rough Sets for the Recommendation Domain in Trust-Based Access Control

Zhao

Xuan

et al. 2015

Algorithms and Architectures for Parallel Processing

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

Cited by 25 publications

References 26 publications

Robust K-Median and K-Means Clustering Algorithms for Incomplete Data

Robust K-Median and K-Means Clustering Algorithms for Incomplete Data

A Global Clustering Approach Using Hybrid Optimization for Incomplete Data Based on Interval Reconstruction of Missing Value

A Clustering Algorithm Based on Rough Sets for the Recommendation Domain in Trust-Based Access Control

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