Projected fuzzy c-means clustering algorithm with instance penalty

At present, high-dimensional data clustering has become a vital research field in machine learning. Traditional clustering algorithms cannot perform well on high-dimensional data, where the clustering task is usually divided into two stages: dimensionality reduction first and clustering later. In ge...

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Vydané v:Expert systems with applications Ročník 255; s. 124563
Hlavní autori: Wang, Jikui, Wu, Yiwen, Huang, Xueyan, Zhang, Cuihong, Nie, Feiping
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.12.2024
Predmet:
Dimensionality reduction
Fuzzy c-means clustering
Instance penalty
Principal component analysis
Dimensionality reduction
Fuzzy c-means clustering
Instance penalty
Principal component analysis
ISSN:0957-4174
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Shrnutí:At present, high-dimensional data clustering has become a vital research field in machine learning. Traditional clustering algorithms cannot perform well on high-dimensional data, where the clustering task is usually divided into two stages: dimensionality reduction first and clustering later. In general, the existing high-dimensional clustering methods usually have the following shortcomings: (1) the two-stage strategy splits the connection between clustering and dimensionality reduction; (2) these algorithms do not consider the impact of anomalous instances in high-dimensional data on clustering performance. Therefore, to address these problems, a projected fuzzy c-means clustering algorithm with instance penalty (PCIP) is proposed. Firstly, we construct an instance penalty matrix and assign an instance penalty coefficient to each sample. Secondly, a model for clustering high-dimensional data is constructed by integrating fuzzy c-means clustering (FCM) and principal component analysis (PCA). The proposed model can perform dimensionality reduction and clustering simultaneously. In addition, the time complexity of the proposed algorithm is linearly related to the number of samples n, which can efficiently deal with large data sets. The proposed PCIP algorithm is verified by experiments using clustering accuracy and normalized mutual information (NMI) as evaluation metrics. The experimental results on 10 image datasets show that the average accuracy and average NMI of the PCIP algorithm are improved by 0.0375 and 0.0275, respectively, compared to the second-ranked algorithm. •The proposed PCIP accomplishes both dimensionality reduction and clustering.•The instance penalty matrix is used to identify and handle the abnormal samples.•We propose an iterative algorithm to solve PCIP and its convergence is proved.•The time complexity of PCIP is linearly related to the number of samples.•Extensive experiments have demonstrated the effectiveness of PCIP.
ISSN:0957-4174
DOI:10.1016/j.eswa.2024.124563