scholarly journals A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7501
Author(s):  
Cunli Mao ◽  
Haoyuan Liang ◽  
Zhengtao Yu ◽  
Yuxin Huang ◽  
Junjun Guo
Keyword(s):  
State Of The Art ◽  
Local Features ◽  
Attention Mechanism ◽  
Clustering Methods ◽  
Clustering Method ◽  
Global Features ◽  
Opinion Analysis ◽  
Large Numbers ◽  
Latent Representations ◽  
Important Basis

Finding the news of same case from the large numbers of case-involved news is an important basis for public opinion analysis. Existing text clustering methods usually based on topic models which only use topic and case infomation as the global features of documents, so distinguishing between different cases with similar types remains a challenge. The contents of documents contain rich local features. Taking into account the internal features of news, the information of cases and the contributions provided by different topics, we propose a clustering method of case-involved news, which combines topic network and multi-head attention mechanism. Using case information and topic information to construct a topic network, then extracting the global features by graph convolution network, thus realizing the combination of case information and topic information. At the same time, the local features are extracted by multi-head attention mechanism. Finally, the fusion of global features and local features is realized by variational auto-encoder, and the learned latent representations are used for clustering. The experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised clustering methods.

scholarly journals Review of single clustering methods

2019 ◽  
Vol 8 (3) ◽  
pp. 221
Author(s):  
Nurshazwani Muhamad Mahfuz ◽  
Marina Yusoff ◽  
Zakiah Ahmad
Keyword(s):  
Data Analytics ◽  
Review Paper ◽  
State Of The Art ◽  
Learning Method ◽  
Clustering Methods ◽  
Optimal Result ◽  
Clustering Method ◽  
Noise Data ◽  
Research Findings ◽  
Real World Problems

<div style="’text-align: justify;">Clustering provides a prime important role as an unsupervised learning method in data analytics to assist many real-world problems such as image segmentation, object recognition or information retrieval. It is often an issue of difficulty for traditional clustering technique due to non-optimal result exist because of the presence of outliers and noise data.  This review paper provides a review of single clustering methods that were applied in various domains.  The aim is to see the potential suitable applications and aspect of improvement of the methods. Three categories of single clustering methods were suggested, and it would be beneficial to the researcher to see the clustering aspects as well as to determine the requirement for clustering method for an employment based on the state of the art of the previous research findings.</div>

scholarly journals Improving Clustering Methods By Exploiting Richness Of Text Data

2021 ◽  
Author(s):  
◽  
Abdul Wahid
Keyword(s):  
Evolutionary Algorithm ◽  
State Of The Art ◽  
Ensemble Methods ◽  
Text Clustering ◽  
Clustering Methods ◽  
Clustering Method ◽  
Clustering Ensemble ◽  
Text Data ◽  
Multi Objective ◽  
User Queries

<p>Clustering is an unsupervised machine learning technique, which involves discovering different clusters (groups) of similar objects in unlabeled data and is generally considered to be a NP hard problem. Clustering methods are widely used in a verity of disciplines for analyzing different types of data, and a small improvement in clustering method can cause a ripple effect in advancing research of multiple fields.  Clustering any type of data is challenging and there are many open research questions. The clustering problem is exacerbated in the case of text data because of the additional challenges such as issues in capturing semantics of a document, handling rich features of text data and dealing with the well known problem of the curse of dimensionality.  In this thesis, we investigate the limitations of existing text clustering methods and address these limitations by providing five new text clustering methods--Query Sense Clustering (QSC), Dirichlet Weighted K-means (DWKM), Multi-View Multi-Objective Evolutionary Algorithm (MMOEA), Multi-objective Document Clustering (MDC) and Multi-Objective Multi-View Ensemble Clustering (MOMVEC). These five new clustering methods showed that the use of rich features in text clustering methods could outperform the existing state-of-the-art text clustering methods.  The first new text clustering method QSC exploits user queries (one of the rich features in text data) to generate better quality clusters and cluster labels.  The second text clustering method DWKM uses probability based weighting scheme to formulate a semantically weighted distance measure to improve the clustering results.  The third text clustering method MMOEA is based on a multi-objective evolutionary algorithm. MMOEA exploits rich features to generate a diverse set of candidate clustering solutions, and forms a better clustering solution using a cluster-oriented approach.  The fourth and the fifth text clustering method MDC and MOMVEC address the limitations of MMOEA. MDC and MOMVEC differ in terms of the implementation of their multi-objective evolutionary approaches.  All five methods are compared with existing state-of-the-art methods. The results of the comparisons show that the newly developed text clustering methods out-perform existing methods by achieving up to 16\% improvement for some comparisons. In general, almost all newly developed clustering algorithms showed statistically significant improvements over other existing methods.  The key ideas of the thesis highlight that exploiting user queries improves Search Result Clustering(SRC); utilizing rich features in weighting schemes and distance measures improves soft subspace clustering; utilizing multiple views and a multi-objective cluster oriented method improves clustering ensemble methods; and better evolutionary operators and objective functions improve multi-objective evolutionary clustering ensemble methods.  The new text clustering methods introduced in this thesis can be widely applied in various domains that involve analysis of text data. The contributions of this thesis which include five new text clustering methods, will not only help researchers in the data mining field but also to help a wide range of researchers in other fields.</p>

scholarly journals Threshold-Based Hierarchical Clustering for Person Re-Identification

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 522
Author(s):  
Minhui Hu ◽  
Kaiwei Zeng ◽  
Yaohua Wang ◽  
Yang Guo
Keyword(s):  
Hierarchical Clustering ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Fine Tuning ◽  
Single Sample ◽  
Clustering Methods ◽  
Clustering Method ◽  
Cluster Number ◽  
Unsupervised Domain Adaptation ◽  
And Training

Unsupervised domain adaptation is a challenging task in person re-identification (re-ID). Recently, cluster-based methods achieve good performance; clustering and training are two important phases in these methods. For clustering, one major issue of existing methods is that they do not fully exploit the information in outliers by either discarding outliers in clusters or simply merging outliers. For training, existing methods only use source features for pretraining and target features for fine-tuning and do not make full use of all valuable information in source datasets and target datasets. To solve these problems, we propose a Threshold-based Hierarchical clustering method with Contrastive loss (THC). There are two features of THC: (1) it regards outliers as single-sample clusters to participate in training. It well preserves the information in outliers without setting cluster number and combines advantages of existing clustering methods; (2) it uses contrastive loss to make full use of all valuable information, including source-class centroids, target-cluster centroids and single-sample clusters, thus achieving better performance. We conduct extensive experiments on Market-1501, DukeMTMC-reID and MSMT17. Results show our method achieves state of the art.

A Joint Approach to Detect Malicious URL Based on Attention Mechanism

2019 ◽  
Vol 18 (03) ◽  
pp. 1950021
Author(s):  
Yongfang Peng ◽  
Shengwei Tian ◽  
Long Yu ◽  
Yalong Lv ◽  
Ruijin Wang
Keyword(s):  
Short Term Memory ◽  
Local Features ◽  
Attention Mechanism ◽  
Global Features ◽  
Combined Model ◽  
Lexical Information ◽  
Softmax Classifier ◽  
Joint Approach ◽  
Deep Learning Model ◽  
Better Than

To improve the accuracy and automation of malware Uniform Resource Locator (URL) recognition, a joint approach of Convolutional neural network (CNN) and Long-short term memory (LSTM) based on the Attention mechanism (JCLA) is proposed to identify and detect malicious URL. Firstly, the URL features including texture information, lexical information and host information are extracted and filtered, and pre-processed with encode. Then, the feature matrix more relevant to the output are chose according to the weight of the attention mechanism and input to the constructed parallel processing model called CNN_LSTM, combinating CNN and LSTM to get local features. Next, the extracted local features are merged to calculate the global features of the URLs to be detected. Finally, the URLs are classified by the SoftMax classifier using global features, the accuracy of the model in malicious URL recgonition is 98.26%. The experimental results show that the JCLA model proposed in this paper is better than the traditional deep learning model or CNN_LSTM combined model for detecting malicious URLs.

scholarly journals Robust Multi-view Learning via Half-quadratic Minimization

2018 ◽  
Author(s):  
Yonghua Zhu ◽  
Xiaofeng Zhu ◽  
Wei Zheng
Keyword(s):  
Error Estimation ◽  
State Of The Art ◽  
Data Distribution ◽  
Experimental Results ◽  
Clustering Methods ◽  
Clustering Method ◽  
Number Of Clusters ◽  
Single View ◽  
Quadratic Minimization ◽  
Quadratic Theory

Although multi-view clustering is capable to usemore information than single view clustering, existing multi-view clustering methods still have issues to be addressed, such as initialization sensitivity, the specification of the number of clusters,and the influence of outliers. In this paper, we propose a robust multi-view clustering method to address these issues. Specifically, we first propose amulti-view based sum-of-square error estimation tomake the initialization easy and simple as well asuse a sum-of-norm regularization to automaticallylearn the number of clusters according to data distribution. We further employ robust estimators constructed by the half-quadratic theory to avoid theinfluence of outliers for conducting robust estimations of both sum-of-square error and the numberof clusters. Experimental results on both syntheticand real datasets demonstrate that our method outperforms the state-of-the-art methods.  

scholarly journals Improving Clustering Methods By Exploiting Richness Of Text Data

2021 ◽  
Author(s):  
◽  
Abdul Wahid
Keyword(s):  
Evolutionary Algorithm ◽  
State Of The Art ◽  
Ensemble Methods ◽  
Text Clustering ◽  
Clustering Methods ◽  
Clustering Method ◽  
Clustering Ensemble ◽  
Text Data ◽  
Multi Objective ◽  
User Queries

<p>Clustering is an unsupervised machine learning technique, which involves discovering different clusters (groups) of similar objects in unlabeled data and is generally considered to be a NP hard problem. Clustering methods are widely used in a verity of disciplines for analyzing different types of data, and a small improvement in clustering method can cause a ripple effect in advancing research of multiple fields.  Clustering any type of data is challenging and there are many open research questions. The clustering problem is exacerbated in the case of text data because of the additional challenges such as issues in capturing semantics of a document, handling rich features of text data and dealing with the well known problem of the curse of dimensionality.  In this thesis, we investigate the limitations of existing text clustering methods and address these limitations by providing five new text clustering methods--Query Sense Clustering (QSC), Dirichlet Weighted K-means (DWKM), Multi-View Multi-Objective Evolutionary Algorithm (MMOEA), Multi-objective Document Clustering (MDC) and Multi-Objective Multi-View Ensemble Clustering (MOMVEC). These five new clustering methods showed that the use of rich features in text clustering methods could outperform the existing state-of-the-art text clustering methods.  The first new text clustering method QSC exploits user queries (one of the rich features in text data) to generate better quality clusters and cluster labels.  The second text clustering method DWKM uses probability based weighting scheme to formulate a semantically weighted distance measure to improve the clustering results.  The third text clustering method MMOEA is based on a multi-objective evolutionary algorithm. MMOEA exploits rich features to generate a diverse set of candidate clustering solutions, and forms a better clustering solution using a cluster-oriented approach.  The fourth and the fifth text clustering method MDC and MOMVEC address the limitations of MMOEA. MDC and MOMVEC differ in terms of the implementation of their multi-objective evolutionary approaches.  All five methods are compared with existing state-of-the-art methods. The results of the comparisons show that the newly developed text clustering methods out-perform existing methods by achieving up to 16\% improvement for some comparisons. In general, almost all newly developed clustering algorithms showed statistically significant improvements over other existing methods.  The key ideas of the thesis highlight that exploiting user queries improves Search Result Clustering(SRC); utilizing rich features in weighting schemes and distance measures improves soft subspace clustering; utilizing multiple views and a multi-objective cluster oriented method improves clustering ensemble methods; and better evolutionary operators and objective functions improve multi-objective evolutionary clustering ensemble methods.  The new text clustering methods introduced in this thesis can be widely applied in various domains that involve analysis of text data. The contributions of this thesis which include five new text clustering methods, will not only help researchers in the data mining field but also to help a wide range of researchers in other fields.</p>

scholarly journals Dual Branch Attention Network for Person Re-Identification

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5839
Author(s):  
Denghua Fan ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Camera Tracking ◽  
Global Features ◽  
Attention Network ◽  
Fine Grained ◽  
Rank List ◽  
Security Problem ◽  
Spatial Dimensions ◽  
Global And Local

As a sub-direction of image retrieval, person re-identification (Re-ID) is usually used to solve the security problem of cross camera tracking and monitoring. A growing number of shopping centers have recently attempted to apply Re-ID technology. One of the development trends of related algorithms is using an attention mechanism to capture global and local features. We notice that these algorithms have apparent limitations. They only focus on the most salient features without considering certain detailed features. People’s clothes, bags and even shoes are of great help to distinguish pedestrians. We notice that global features usually cover these important local features. Therefore, we propose a dual branch network based on a multi-scale attention mechanism. This network can capture apparent global features and inconspicuous local features of pedestrian images. Specifically, we design a dual branch attention network (DBA-Net) for better performance. These two branches can optimize the extracted features of different depths at the same time. We also design an effective block (called channel, position and spatial-wise attention (CPSA)), which can capture key fine-grained information, such as bags and shoes. Furthermore, based on ID loss, we use complementary triplet loss and adaptive weighted rank list loss (WRLL) on each branch during the training process. DBA-Net can not only learn semantic context information of the channel, position, and spatial dimensions but can integrate detailed semantic information by learning the dependency relationships between features. Extensive experiments on three widely used open-source datasets proved that DBA-Net clearly yielded overall state-of-the-art performance. Particularly on the CUHK03 dataset, the mean average precision (mAP) of DBA-Net achieved 83.2%.

Critical Technical Issues in High Voltage SiC Power Devices

2008 ◽  
Vol 600-603 ◽  
pp. 895-900 ◽  
Author(s):  
Anant K. Agarwal ◽  
Albert A. Burk ◽  
Robert Callanan ◽  
Craig Capell ◽  
Mrinal K. Das ◽  
...  
Keyword(s):  
Threshold Voltage ◽  
Carrier Mobility ◽  
Negative Charge ◽  
State Of The Art ◽  
Power Devices ◽  
Power Mosfets ◽  
Large Numbers ◽  
Technical Issues ◽  
The Difference ◽  
Difficult Issue

In this paper, we review the state of the art of SiC switches and the technical issues which remain. Specifically, we will review the progress and remaining challenges associated with SiC power MOSFETs and BJTs. The most difficult issue when fabricating MOSFETs has been an excessive variation in threshold voltage from batch to batch. This difficulty arises due to the fact that the threshold voltage is determined by the difference between two large numbers, namely, a large fixed oxide charge and a large negative charge in the interface traps. There may also be some significant charge captured in the bulk traps in SiC and SiO2. The effect of recombination-induced stacking faults (SFs) on majority carrier mobility has been confirmed with 10 kV Merged PN Schottky (MPS) diodes and MOSFETs. The same SFs have been found to be responsible for degradation of BJTs.

scholarly journals A Global-Local Blur Disentangling Network for Dynamic Scene Deblurring

2021 ◽  
Vol 11 (5) ◽  
pp. 2174
Author(s):  
Xiaoguang Li ◽  
Feifan Yang ◽  
Jianglu Huang ◽  
Li Zhuo
Keyword(s):  
Local Features ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Dynamic Scene ◽  
Feature Maps ◽  
Training Scheme ◽  
Real Scene ◽  
Global And Local

Images captured in a real scene usually suffer from complex non-uniform degradation, which includes both global and local blurs. It is difficult to handle the complex blur variances by a unified processing model. We propose a global-local blur disentangling network, which can effectively extract global and local blur features via two branches. A phased training scheme is designed to disentangle the global and local blur features, that is the branches are trained with task-specific datasets, respectively. A branch attention mechanism is introduced to dynamically fuse global and local features. Complex blurry images are used to train the attention module and the reconstruction module. The visualized feature maps of different branches indicated that our dual-branch network can decouple the global and local blur features efficiently. Experimental results show that the proposed dual-branch blur disentangling network can improve both the subjective and objective deblurring effects for real captured images.

scholarly journals Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou
Keyword(s):  
Spatial Analysis ◽  
Spatial Clustering ◽  
Heat Index ◽  
Operating Efficiency ◽  
Clustering Methods ◽  
Clustering Method ◽  
Trajectory Mining ◽  
Density Peaks ◽  
Analysis Methods ◽  
Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

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