scholarly journals Outlier Detection of Light Buoy Telemetry and Telecontrol Data Based on Improved Adaptive ε Neighborhood DBSCAN Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Liangkun Xu ◽  
Yongxing Jin ◽  
Han Xue ◽  
Shibo Zhou
Keyword(s):  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Water Area ◽  
Main Channel ◽  
Hot Water ◽  
Migration Process ◽  
Dbscan Clustering ◽  
Noise Point ◽  
Position Data ◽  
The Common

In this paper, according to the water area of light buoy, the migration rule of light buoy in main channel is counted, and the frequency of light buoy passing through a certain position point in the process of migration is calculated, and the model is verified by buoy position data. An anomaly detection algorithm based on improved adaptive DBSCAN clustering is designed. The size of the ε neighborhood is adaptive according to the wind speed, wave height, and drift distance span of the water area where the light buoy is located. The experimental results show that the improved adaptive DBSCAN clustering algorithm can solve the problem that the common DBSCAN clustering algorithm takes the “hot” water area of the light buoy position or the most likely area in the light buoy migration process as the noise point.

scholarly journals Community Detection Based on Graph Representation Learning in Evolutionary Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4497
Author(s):  
Dongming Chen ◽  
Mingshuo Nie ◽  
Jie Wang ◽  
Yun Kong ◽  
Dongqi Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Network Structure ◽  
Clustering Algorithm ◽  
Laplacian Matrix ◽  
Representation Learning ◽  
Detection Algorithm ◽  
Graph Representation ◽  
Time Slice ◽  
Current Time ◽  
Evolutionary Networks

Aiming at analyzing the temporal structures in evolutionary networks, we propose a community detection algorithm based on graph representation learning. The proposed algorithm employs a Laplacian matrix to obtain the node relationship information of the directly connected edges of the network structure at the previous time slice, the deep sparse autoencoder learns to represent the network structure under the current time slice, and the K-means clustering algorithm is used to partition the low-dimensional feature matrix of the network structure under the current time slice into communities. Experiments on three real datasets show that the proposed algorithm outperformed the baselines regarding effectiveness and feasibility.

scholarly journals Resolvable Cluster Target Tracking Based on the DBSCAN Clustering Algorithm and Labeled RFS

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 43364-43377
Author(s):  
Xirui Xue ◽  
Shucai Huang ◽  
Jiahao Xie ◽  
Jiashun Ma ◽  
Ning Li
Keyword(s):  
Target Tracking ◽  
Clustering Algorithm ◽  
Dbscan Clustering

The Social Psychology of Homophily: The Collective Sentiments of Education Advocacy Groups

2020 ◽  
Vol 122 (6) ◽  
pp. 1-32
Author(s):  
Jonathan A. Supovitz ◽  
Christian Kolouch ◽  
Alan J. Daly
Keyword(s):  
Social Network ◽  
Common Core ◽  
Detection Algorithm ◽  
Psychological Characteristics ◽  
Thinking Style ◽  
Advocacy Groups ◽  
Narrative Thinking ◽  
The Social ◽  
The Common ◽  
Community Detection Algorithm

Background/Context As a major area of civic decision making, public education is a central arena for advocacy groups seeking to influence policy debates. An emerging body of research examines advocates’ use of social media. While debates about policy can be thought of as a clash of large ideas contained within frames, cognitive linguists note that framing strategies are activated by the particular words that advocates choose to convey their positions. Purpose/Objective/Research Question/Focus of Study This study examined the vociferous debate surrounding the Common Core State Standards on Twitter during the height of state adoption in 2014 and 2015. Combining social network analysis and natural language processing techniques, we first identified the organically forming factions within the Common Core debate on Twitter and then captured the collective psychological sentiments of these factions. Research Design The study employed quantitative statistical comparisons of the frequency of words used by members of different factions around the Common Core on Twitter that are associated in prior research with four psychological characteristics: mood, motivation, conviction, and thinking style. Data Collection and Analysis Data were downloaded from Twitter from November 2014 to October 2015 using at least one of three hashtags: #commoncore, #ccss, or #stopcommoncore. The resulting data set consisted of more than 500,000 tweets and retweets from more than 100,000 distinct actors. We then ran a community detection algorithm to identify the structural subcommunities, or factions. To measure the four psychological characteristics, we adapted Pennebaker and colleagues’ Linguistic Inquiry and Word Count libraries. We then connected the individual tweet authors to their faction based on the results of the social network analysis community detection algorithm. Using these groups, and the standardized results for each psychological characteristic/dimension, we performed a series of analyses of variance with Bonferroni corrections to test for differences in the psychological characteristics among the factions. Findings/Results For each of the four psychological characteristics, we found different patterns among the different factions. Educators opposed to the Common Core had the highest level of drive motivation, use of sad words, and use of words associated with a narrative thinking style. Opponents of the Common Core from outside education exhibited an affiliative drive motivation, a narrative thinking style, high levels of anger words, and low levels of conviction in their choice of language. Supporters of the Common Core used words that represented a more analytic thinking style, stronger levels of conviction, and words associated with a higher level of achievement orientation. Conclusions/Recommendations Individuals on Twitter, mostly strangers to each other, band together to form fluid communities as they share positions on particular issues. On Twitter, these bonds are formed by behavioral choices to follow, retweet, and mention others. This study reveals how like-minded individuals create a collective sentiment through their specific choice of words to express their views. By analyzing the underlying psychological characteristics associated with language, we show the distinct pooled psychologies of activists as they engaged together in political activity in an effort to influence the political environment.

scholarly journals AN EFFICIENT CLUSTERING METHOD FOR DBSCAN GEOGRAPHIC SPATIO-TEMPORAL LARGE DATA WITH IMPROVED PARAMETER OPTIMIZATION

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 581-584
Author(s):  
J. W. Li ◽  
X. Q. Han ◽  
J. W. Jiang ◽  
Y. Hu ◽  
L. Liu
Keyword(s):  
Parameter Optimization ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Large Data ◽  
Parameter Selection ◽  
Physical Analysis ◽  
Clustering Method ◽  
K Value ◽  
Dbscan Clustering ◽  
Spatio Temporal

Abstract. How to establish an effective method of large data analysis of geographic space-time and quickly and accurately find the hidden value behind geographic information has become a current research focus. Researchers have found that clustering analysis methods in data mining field can well mine knowledge and information hidden in complex and massive spatio-temporal data, and density-based clustering is one of the most important clustering methods.However, the traditional DBSCAN clustering algorithm has some drawbacks which are difficult to overcome in parameter selection. For example, the two important parameters of Eps neighborhood and MinPts density need to be set artificially. If the clustering results are reasonable, the more suitable parameters can not be selected according to the guiding principles of parameter setting of traditional DBSCAN clustering algorithm. It can not produce accurate clustering results.To solve the problem of misclassification and density sparsity caused by unreasonable parameter selection in DBSCAN clustering algorithm. In this paper, a DBSCAN-based data efficient density clustering method with improved parameter optimization is proposed. Its evaluation index function (Optimal Distance) is obtained by cycling k-clustering in turn, and the optimal solution is selected. The optimal k-value in k-clustering is used to cluster samples. Through mathematical and physical analysis, we can determine the appropriate parameters of Eps and MinPts. Finally, we can get clustering results by DBSCAN clustering. Experiments show that this method can select parameters reasonably for DBSCAN clustering, which proves the superiority of the method described in this paper.

scholarly journals Analysis of new nosological models from disease similarities using clustering

2020 ◽  
Author(s):  
Lucía Prieto Santamaría ◽  
Eduardo P. García del Valle ◽  
Gerardo Lagunes García ◽  
Massimiliano Zanin ◽  
Alejandro Rodríguez González ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Molecular Data ◽  
Modern Medicine ◽  
Biological Information ◽  
Distributed Data ◽  
Feature Vectors ◽  
Dbscan Clustering ◽  
Disease Similarity ◽  
Starting Point ◽  
Evaluation Metric

AbstractWhile classical disease nosology is based on phenotypical characteristics, the increasing availability of biological and molecular data is providing new understanding of diseases and their underlying relationships, that could lead to a more comprehensive paradigm for modern medicine. In the present work, similarities between diseases are used to study the generation of new possible disease nosologic models that include both phenotypical and biological information. To this aim, disease similarity is measured in terms of disease feature vectors, that stood for genes, proteins, metabolic pathways and PPIs in the case of biological similarity, and for symptoms in the case of phenotypical similarity. An improvement in similarity computation is proposed, considering weighted instead of Booleans feature vectors. Unsupervised learning methods were applied to these data, specifically, density-based DBSCAN clustering algorithm. As evaluation metric silhouette coefficient was chosen, even though the number of clusters and the number of outliers were also considered. As a results validation, a comparison with randomly distributed data was performed. Results suggest that weighted biological similarities based on proteins, and computed according to cosine index, may provide a good starting point to rearrange disease taxonomy and nosology.

scholarly journals Pedestrian detection algorithm based on improved muti-scale feature fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012008
Author(s):  
Hui Liu ◽  
Keyang Cheng
Keyword(s):  
Clustering Algorithm ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Data Sets ◽  
False Detection ◽  
Scale Feature ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Small Targets

Abstract Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.

scholarly journals Lane Formation Beyond Intuition Towards an Automated Characterization of Lanes in Counter-flows

2020 ◽  
Vol 5 ◽  
Author(s):  
Luca Crociani ◽  
Giuseppe Vizzari ◽  
Andrea Gorrini ◽  
Stefania Bandini
Keyword(s):  
Clustering Algorithm ◽  
Variable Density ◽  
Automatic Identification ◽  
Computing Power ◽  
Dbscan Clustering ◽  
Significant Difference ◽  
Lane Formation ◽  
Human Coder ◽  
Behavioural Dynamics

Pedestrian behavioural dynamics have been growingly investigated by means of (semi)automated computing techniques for almost two decades, exploiting advancements on computing power, sensor accuracy and availability, computer vision algorithms. This has led to a unique consensus on the existence of significant difference between unidirectional and bidirectional flows of pedestrians, where the phenomenon of lane formation seems to play a major role. The collective behaviour of lane formation emerges in condition of variable density and due to a self-organisation dynamic, for which pedestrians are induced to walk following preceding persons to avoid and minimize conflictual situations. Although the formation of lanes is a well-known phenomenon in this field of study, there is still a lack of methods offering the possibility to provide an (even semi-) automatic identification and a quantitative characterization. In this context, the paper proposes an unsupervised learning approach for an automatic detection of lanes in multi-directional pedestrian flows, based on the DBSCAN clustering algorithm. The reliability of the approach is evaluated through an inter-rater agreement test between the results achieved by a human coder and by the algorithm.

A novel algorithm for community detection based on resistance distance and similarity

2021 ◽  
pp. 2150164
Author(s):  
Pengli Lu ◽  
Zhou Yu ◽  
Yuhong Guo
Keyword(s):  
Community Detection ◽  
Distance Function ◽  
Nearest Neighbor ◽  
Detection Algorithm ◽  
Structure And Function ◽  
Resistance Distance ◽  
Node Similarity ◽  
The Common ◽  
Community Detection Algorithm ◽  
And Function

Community detection is important for understanding the structure and function of networks. Resistance distance is a kind of distance function inherent in the network itself, which has important applications in many fields. In this paper, we propose a novel community detection algorithm based on resistance distance and similarity. First, we propose the node similarity, which is based on the common nodes and resistance distance. Then, we define the distance function between nodes by similarity. Furthermore, we calculate the distance between communities by using the distance between nodes. Finally, we detect the community structure in the network according to the nearest-neighbor nodes being in the same community. Experimental results on artificial networks and real-world networks show that the proposed algorithm can effectively detect the community structures in complex networks.

Authorship Detection and Encoding for eBay Images

2011 ◽  
Vol 2 (1) ◽  
pp. 22-37 ◽  
Author(s):  
Liping Zhou ◽  
Wei-Bang Chen ◽  
Chengcui Zhang
Keyword(s):  
Clustering Algorithm ◽  
Classification Methods ◽  
Test Image ◽  
Common Edge ◽  
Probability Approach ◽  
Color Models ◽  
The Common ◽  
Edge Based

This paper describes a framework to detect authorship of eBay images. It contains three modules: editing style summarization, classification and multi-account linking detection. For editing style summarization, three approaches, namely the edge-based approach, the color-based approach, and the color probability approach, are proposed to encode the common patterns inside a group of images with similar editing styles into common edge or color models. Prior to the summarization step, an edge-based clustering algorithm is developed. Corresponding to the three summarization approaches, three classification methods are developed accordingly to predict the authorship of an unlabeled test image. For multi-account linking detection, to detect the hidden owner behind multiple eBay seller accounts, two methods to measure the similarity between seller accounts based on similar models are presented.

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