A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

Yi, Junkai; Zhang, Yacong; Zhao, Xianghui; Wan, Jing

doi:10.1155/2017/8310934

Cited by 14 publications

(7 citation statements)

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“…The test results demonstrate that the proposed approach is giving better outcomes as far as exactness, better positive negative rate. 3 [32] Text clustering approach using deeplearning vocabulary network They presented a novel approach named deep-learning vocabulary network. Their vocabulary network has been constructed based on related-word set.…”

Section: Sno Reference Methodsmentioning

confidence: 99%

A survey and analysis based on topic based classification

Sikarwar¹,

Patidar²,

Kushwah³

2018

IJATEE

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In this paper a survey and analysis based on topic based data classification has been presented. It includes the topic based data orientation, data categorization, document clustering, etc. This study provides the analytical way to analyze the methods previously published and provide explorative way of the approaches presented. It also provides the discussion based on the attributes and property used and explored. This study provides the discussion of different partitioning algorithm, different grouping methods and classification approaches.Based on the study, future enhancements have been suggested.

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Section: Sno Reference Methodsmentioning

confidence: 99%

A survey and analysis based on topic based classification

Sikarwar¹,

Patidar²,

Kushwah³

2018

IJATEE

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“…Standard category labels of the input data are obtained on the Soft-Max of the DBN top layer [26,27]. The DBN does not rely on the manual selection, and it learns the input data actively and digs out rich information hidden in the known data automatically [28].…”

Section: Deep Belief Network (Dbn)mentioning

confidence: 99%

The Face Recognition Method Based on CS-LBP and DBN

Sun

Yin

Yang

et al. 2018

Mathematical Problems in Engineering

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At present, the face recognition method based on deep belief network (DBN) has advantages of automatically learning the abstract information of face images and being affected slightly by active factors, so it becomes the main method in the face recognition area. Because DBN ignores the local information of face images, the face recognition rate based on DBN is badly affected. To solve this problem, a face recognition method based on center-symmetric local binary pattern (CS-LBP) and DBN (FRMCD) is proposed in this paper. Firstly, the face image is divided into several subblocks. Secondly, CS-LBP is used to extract texture features of each image subblock. Thirdly, texture feature histograms are formed and input into the DBN visual layer. Finally, face classification and face recognition are completed through deep learning in DBN. Through the experiments on face databases ORL, Extend Yale B, and CMU-PIE by the proposed method (FRMCD), the best partitioning way of the face image and the hidden unit number of the DBN hidden layer are obtained. Then, comparative experiments between the FRMCD and traditional methods are performed. The results show that the recognition rate of FRMCD is superior to those of traditional methods; the highest recognition rate is up to 98.82%. When the number of training samples is less, the FRMCD has more significant advantages. Compared with the method based on local binary pattern (LBP) and DBN, the time-consuming of FRMCD is shorter.

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“…It is a technique that separates data points/samples into groups/clusters so that these data points/samples are more related to other data points/samples in the same groups/clusters than those in the other groups/clusters. Its applications are huge such as mobility pattern clustering [1], text clustering [2]- [5], and customer segmentation [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

Hypergraph convolutional neural network-based clustering technique

Tran

Trinh

Tran

2022

IJ-AI

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This paper constitutes the novel hypergraph convolutional neural networkbased clustering technique. This technique is employed to solve the clustering problem for the Citeseer dataset and the Cora dataset. Each dataset contains the feature matrix and the incidence matrix of the hypergraph (i.e., constructed from the feature matrix). This novel clustering method utilizes both matrices. Initially, the hypergraph auto-encoders are employed to transform both the incidence matrix and the feature matrix from high dimensional space to low dimensional space. In the end, we apply the k-means clustering technique to the transformed matrix. The hypergraph convolutional neural network (CNN)-based clustering technique presented a better result on performance during experiments than those of the other classical clustering techniques.

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A Novel Text Clustering Approach Using Deep-Learning Vocabulary Network

Cited by 14 publications

References 50 publications

A survey and analysis based on topic based classification

A survey and analysis based on topic based classification

The Face Recognition Method Based on CS-LBP and DBN

Hypergraph convolutional neural network-based clustering technique

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