Multi-view document clustering via ensemble method

Hussain, Syed Fawad; Mushtaq, Muhammad; Halim, Zahid

doi:10.1007/s10844-014-0307-6

Cited by 55 publications

(23 citation statements)

References 23 publications

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“…Ensemble clustering combines the multiple results of different clustering algorithms to obtain final results. Multiview clustering is an extension of ensemble clustering and combines different data that have different properties and views [34,35].…”

Section: Clustering Algorithmmentioning

confidence: 99%

A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

Zhang

Zhao

et al. 2017

Mathematical Problems in Engineering

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Text clustering is an effective approach to collect and organize text documents into meaningful groups for mining valuable information on the Internet. However, there exist some issues to tackle such as feature extraction and data dimension reduction. To overcome these problems, we present a novel approach named deep-learning vocabulary network. The vocabulary network is constructed based on related-word set, which contains the “cooccurrence” relations of words or terms. We replace term frequency in feature vectors with the “importance” of words in terms of vocabulary network and PageRank, which can generate more precise feature vectors to represent the meaning of text clustering. Furthermore, sparse-group deep belief network is proposed to reduce the dimensionality of feature vectors, and we introduce coverage rate for similarity measure in Single-Pass clustering. To verify the effectiveness of our work, we compare the approach to the representative algorithms, and experimental results show that feature vectors in terms of deep-learning vocabulary network have better clustering performance.

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Section: Clustering Algorithmmentioning

confidence: 99%

A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

Zhang

Zhao

et al. 2017

Mathematical Problems in Engineering

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“…The methods in the first category create a fusion of the multiview information in the early stage and then perform clustering [ 4 – 6 ]. The methods in the second category group samples in each view and then create a late fusion of the clustering results from different views to obtain the final clustering decision [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Incomplete Multiview Clustering via Late Fusion

Liu

et al. 2018

Computational Intelligence and Neuroscience

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In real-world applications of multiview clustering, some views may be incomplete due to noise, sensor failure, etc. Most existing studies in the field of incomplete multiview clustering have focused on early fusion strategies, for example, learning subspace from multiple views. However, these studies overlook the fact that clustering results with the visible instances in each view could be reliable under the random missing assumption; accordingly, it seems that learning a final clustering decision via late fusion of the clustering results from incomplete views would be more natural. To this end, we propose a late fusion method for incomplete multiview clustering. More specifically, the proposed method performs kernel k-means clustering on the visible instances in each view and then performs a late fusion of the clustering results from different views. In the late fusion step of the proposed method, we encode each view's clustering result as a zero-one matrix, of which each row serves as a compressed representation of the corresponding instance. We then design an alternate updating algorithm to learn a unified clustering decision that can best group the visible compressed representations in each view according to the k-means clustering objective. We compare the proposed method with several commonly used imputation methods and a representative early fusion method on six benchmark datasets. The superior clustering performance observed validates the effectiveness of the proposed method.

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“…Examples of clustering methods categories are hierarchical methods, density-based methods, grid-based methods, model-based methods and partitional methods (Jain et al 1999). These clustering methods were well used in several applications such as intrusion detection (Tsai et al 2009;Wang et al 2010), customer segmentation (Liu and Ong 2008), document clustering (Ben N'Cir and Essoussi 2015; Hussain et al 2014), image organization (Ayech and Ziou 2015;Du et al 2015). In fact, conventional clustering methods are not suitable when dealing with large scale data.…”

Section: Introductionmentioning

confidence: 99%

One-pass MapReduce-based clustering method for mixed large scale data

2017

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Big data is often characterized by a huge volume and a mixed types of attributes namely, numeric and categorical. K-prototypes has been fitted into MapReduce framework and hence it has become a solution for clustering mixed large scale data. However, k-prototypes requires computing all distances between each of the cluster centers and the data points. Many of these distance computations are redundant, because data points usually stay in the same cluster after first few iterations. Also, k-prototypes is not suitable for running within MapReduce framework: the iterative nature of k-prototypes cannot be modeled through MapReduce since at each iteration of k-prototypes, the whole data set must be read and written to disks and this results a high input/output (I/O) operations. To deal with these issues, we propose a new one-pass accelerated MapReduce-based k-prototypes clustering method for mixed large scale data. The proposed method reads and writes data only once which reduces largely the I/O operations compared to existing MapReduce implementation of k-prototypes. Furthermore, the proposed method is based on a pruning strategy to accelerate the clustering process by reducing the redundant distance computations between cluster centers and data points. Experiments performed on simulated and real data sets show that the proposed method is scalable and improves the efficiency of the existing k-prototypes methods.

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Multi-view document clustering via ensemble method

Cited by 55 publications

References 23 publications

A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

Incomplete Multiview Clustering via Late Fusion

One-pass MapReduce-based clustering method for mixed large scale data

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