An incremental affinity propagation algorithm and its applications for text clustering

Shi, Xiaoning; Guan, Renchu; Wang, L.P.; Pei, Zhili

doi:10.1109/ijcnn.2009.5178973

Cited by 11 publications

(15 citation statements)

References 17 publications

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“…Although there are many types of clustering algorithms, we focus on exemplar-based clustering algorithm in this paper due to its wide applications and outstanding advantages [10,[21][22][23][24]. By exemplars we mean those cluster centers are chosen from actual data.…”

Section: Introductionmentioning

confidence: 99%

“…Exemplar-based clustering algorithms have gained numerous achievements in recent years [17][18][19][20][21][22][23][24]. However, these algorithms need to store a similarity matrix of the entire dataset, which indeed limits the ability of exemplarbased algorithms for processing large data.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we work on extending the typical exemplarbased clustering algorithm EEM into an incremental version to process large datasets chunk by chunk. Although several incremental versions of AP have been proposed in [21][22][23][24], they aimed at dealing with dynamic datasets or semi-supervised clustering instead of large datasets.…”

Section: Introductionmentioning

confidence: 99%

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Incremental enhanced α-expansion move for large data: a probability regularization perspective

Wang

2016

Int. J. Mach. Learn. & Cyber.

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To deal with large data clustering tasks, an incremental version of exemplar-based clustering algorithm is proposed in this paper. The novel clustering algorithm, called Incremental Enhanced a-Expansion Move (IEEM), processes large data chunk by chunk. The work here includes two aspects. First, in terms of the maximum a posteriori principle, a unified target function is developed to unify two typical exemplar-based clustering algorithms, namely Affinity Propagation (AP) and Enhanced aExpansion Move (EEM). Secondly, with the proposed target function, the probability based regularization term is proposed and accordingly the proposed target function is extended to make IEEM have the ability to improve clustering performance of the entire dataset by leveraging the clustering result of previous chunks. Another outstanding characteristic of IEEM is that only by modifying the definitions of several variables used in EEM, the minimization procedure of EEM and its theoretical spirit can be easily kept in IEEM, and hence no more efforts are needed to develop a new optimization algorithm for IEEM. In contrast to AP, EEM and the existing incremental clustering algorithm IMMFC, our experimental results of synthetic and real-world datasets indicate the effectiveness of IEEM.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Incremental enhanced α-expansion move for large data: a probability regularization perspective

Wang

2016

Int. J. Mach. Learn. & Cyber.

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“…To test the only effect of the feature space learning procedure, the result of FSAP is compared with that of IAP in Shi et al 2009. The F-measure and entropy comparison results are shown in Tables 4 and 5.…”

Section: Fsl Vs Incremental Semi-supervised Algorithmmentioning

confidence: 99%

“…(Jain et al 1999;Huang et al 2018;Wu et al 2018;Guan et al 2011) Semi-supervised clustering Pros: Use the information both from labeled data and unlabeled data. Cons: Unlabeled data should be explored carefully and it performed without feature learning procedure Li and Zhou (2015); Tang et al (2007);Wang et al (2012); Guan et al (2011);Shi et al (2009);Xue et al (2011) effectiveness of the proposed algorithms. As a result, topical feature space for each cluster can be found accompanying the end of clustering.…”

Section: Introductionmentioning

confidence: 99%

Feature space learning model

Guan

Wang

Marchese

et al. 2018

J Ambient Intell Human Comput

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With the massive volume and rapid increasing of data, feature space study is of great importance. To avoid the complex training processes in deep learning models which project original feature space into low-dimensional ones, we propose a novel feature space learning (FSL) model. The main contributions in our approach are: (1) FSL can not only select useful features but also adaptively update feature values and span new feature spaces; (2) four FSL algorithms are proposed with the feature space updating procedure; (3) FSL can provide a better data understanding and learn descriptive and compact feature spaces without the tough training for deep architectures. Experimental results on benchmark data sets demonstrate that FSL-based algorithms performed better than the classical unsupervised, semi-supervised learning and even incremental semi-supervised algorithms. In addition, we show a visualization of the learned feature space results. With the carefully designed learning strategy, FSL dynamically disentangles explanatory factors, depresses the noise accumulation and semantic shift, and constructs easy-to-understand feature spaces.

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Microscopic image segmentation approach based on modified affinity propagation-based clustering

Chakraborty,

Mali

2024

Multimed Tools Appl

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An incremental affinity propagation algorithm and its applications for text clustering

Cited by 11 publications

References 17 publications

Incremental enhanced α-expansion move for large data: a probability regularization perspective

Incremental enhanced α-expansion move for large data: a probability regularization perspective

Feature space learning model

Microscopic image segmentation approach based on modified affinity propagation-based clustering

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