scholarly journals Important Location Identification and Personal Location Inference Based on Mobile Subscriber Location Data Preparation of Camera-Ready Contributions to SCITEPRESS Proceedings

2018 ◽  
Vol 173 ◽  
pp. 03086 ◽  
Author(s):  
Zhen Yang ◽  
Wang Hong-jun
Keyword(s):  
Language Processing ◽  
Clustering Algorithm ◽  
Smart Cities ◽  
Mobile Terminal ◽  
Trajectory Data ◽  
Density Peak ◽  
Data Set ◽  
Location Data ◽  
Or Groups ◽  
Density Peak Clustering

As an emerging spatial trajectory data, mobile terminal location data can be widely used to analyze the behavior characteristics and interests of individuals or groups in smart cities, transportation planning and other civil fields. It can also be used to track suspects in anti-terrorism security and public opinion management. Aiming at the problem that it is difficult to determine suitable input parameters of clustering caused by different subscriber location data size and distribution difference, an improved density peak clustering algorithm is proposed and the performance of the improved algorithm is verified on the UCI data set. Firstly the important location is identified by the proposed algorithm, and the personal location is further inferred by the algorithm based on the subscriber's schedule and maximum cluster. Then, the algorithm adopts Google's inverse geocoding technology to obtain the semantic names corresponding to the coordinate points, and introduces the natural language processing technology to achieve word frequency statistics and keyword extraction. The simulation results based on the Geolife data set show that the algorithm is feasible for identifying important locations and inferring personal locations.

scholarly journals A Fast Density Peak Clustering Algorithm Optimized by Uncertain Number Neighbors for Breast MR Image

2019 ◽  
Vol 1229 ◽  
pp. 012024 ◽  
Author(s):  
Fan Hong ◽  
Yang Jing ◽  
Hou Cun-cun ◽  
Zhang Ke-zhen ◽  
Yao Ruo-xia
Keyword(s):  
Clustering Algorithm ◽  
Mr Image ◽  
Density Peak ◽  
Breast Mr ◽  
Density Peak Clustering

Parallel grid-based density peak clustering of big trajectory data

2021 ◽  
Author(s):  
Xinzheng Niu ◽  
Yunhong Zheng ◽  
Philippe Fournier-Viger ◽  
Bing Wang
Keyword(s):  
Trajectory Data ◽  
Density Peak ◽  
Density Peak Clustering ◽  
Grid Based

scholarly journals Next Location Prediction Based on an Adaboost-Markov Model of Mobile Users

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1475 ◽  
Author(s):  
Hongjun Wang ◽  
Zhen Yang ◽  
Yingchun Shi
Keyword(s):  
Markov Model ◽  
Clustering Algorithm ◽  
Smart Cities ◽  
Prediction Performance ◽  
Mobile Users ◽  
Feature Points ◽  
Trajectory Data ◽  
Adaboost Algorithm ◽  
Target User ◽  
Better Than

As an emerging class of spatial trajectory data, mobile user trajectory data can be used to analyze individual or group behavioral characteristics, hobbies and interests. Besides, the information extracted from original trajectory data is widely used in smart cities, transportation planning, and anti-terrorism maintenance. In order to identify the important locations of the target user from his trajectory data, a novel division method for preprocessing trajectory data is proposed, the feature points of original trajectory are extracted according to the change of trajectory structural, and then important locations are extracted by clustering the feature points, using an improved density peak clustering algorithm. Finally, in order to predict next location of mobile users, a multi-order fusion Markov model based on the Adaboost algorithm is proposed, the model order k is adaptively determined, and the weight coefficients of the 1~k-order models are given by the Adaboost algorithm according to the importance of various order models, a multi-order fusion Markov model is generated to predict next important location of the user. The experimental results on the real user trajectory dataset Geo-life show that the prediction performance of Adaboost-Markov model is better than the multi-order fusion Markov model with equal coefficient, and the universality and prediction performance of Adaboost-Markov model is better than the first to third order Markov models.

scholarly journals Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering

2019 ◽  
Vol 33 ◽  
pp. 4755-4762 ◽  
Author(s):  
Xiaoyu Qin ◽  
Kai Ming Ting ◽  
Ye Zhu ◽  
Vincent CS Lee
Keyword(s):  
Clustering Algorithm ◽  
Distance Measure ◽  
Nearest Neighbour ◽  
Density Peak ◽  
Density Based Clustering ◽  
New Type ◽  
Density Peak Clustering ◽  
The Impact ◽  
First Time ◽  
Tree Method

A recent proposal of data dependent similarity called Isolation Kernel/Similarity has enabled SVM to produce better classification accuracy. We identify shortcomings of using a tree method to implement Isolation Similarity; and propose a nearest neighbour method instead. We formally prove the characteristic of Isolation Similarity with the use of the proposed method. The impact of Isolation Similarity on densitybased clustering is studied here. We show for the first time that the clustering performance of the classic density-based clustering algorithm DBSCAN can be significantly uplifted to surpass that of the recent density-peak clustering algorithm DP. This is achieved by simply replacing the distance measure with the proposed nearest-neighbour-induced Isolation Similarity in DBSCAN, leaving the rest of the procedure unchanged. A new type of clusters called mass-connected clusters is formally defined. We show that DBSCAN, which detects density-connected clusters, becomes one which detects mass-connected clusters, when the distance measure is replaced with the proposed similarity. We also provide the condition under which mass-connected clusters can be detected, while density-connected clusters cannot.

A privacy‐preserving density peak clustering algorithm in cloud computing

2020 ◽  
Vol 32 (11) ◽  
Author(s):  
Liping Sun ◽  
Shang Ci ◽  
Xiaoqing Liu ◽  
Xiaoyao Zheng ◽  
Qingying Yu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Clustering Algorithm ◽  
Privacy Preserving ◽  
Density Peak ◽  
Density Peak Clustering

scholarly journals Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1168
Author(s):  
Jun-Lin Lin ◽  
Jen-Chieh Kuo ◽  
Hsing-Wang Chuang
Keyword(s):  
Clustering Algorithm ◽  
Academic Community ◽  
Performance Study ◽  
Potential Density ◽  
Cluster Assignment ◽  
Density Peak ◽  
Single Linkage ◽  
Density Peaks ◽  
Assignment Strategy ◽  
Density Peak Clustering

Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.

scholarly journals VDPC: Variational Density Peak Clustering Algorithm

2021 ◽  
Author(s):  
Yizhang Wang ◽  
Di Wang ◽  
You Zhou ◽  
Chai Quek ◽  
Xiaofeng Zhang
Keyword(s):  
Clustering Algorithm ◽  
Cluster Formation ◽  
Clustering Algorithms ◽  
Data Distribution ◽  
Distribution Patterns ◽  
Clustering Methods ◽  
Density Peak ◽  
Global Parameter ◽  
Density Peak Clustering ◽  
Parameter Values

<div>Clustering is an important unsupervised knowledge acquisition method, which divides the unlabeled data into different groups \cite{atilgan2021efficient,d2021automatic}. Different clustering algorithms make different assumptions on the cluster formation, thus, most clustering algorithms are able to well handle at least one particular type of data distribution but may not well handle the other types of distributions. For example, K-means identifies convex clusters well \cite{bai2017fast}, and DBSCAN is able to find clusters with similar densities \cite{DBSCAN}. </div><div>Therefore, most clustering methods may not work well on data distribution patterns that are different from the assumptions being made and on a mixture of different distribution patterns. Taking DBSCAN as an example, it is sensitive to the loosely connected points between dense natural clusters as illustrated in Figure~\ref{figconnect}. The density of the connected points shown in Figure~\ref{figconnect} is different from the natural clusters on both ends, however, DBSCAN with fixed global parameter values may wrongly assign these connected points and consider all the data points in Figure~\ref{figconnect} as one big cluster.</div>

scholarly journals Optimization of Density Peak Clustering Algorithm Based on OpenMP

2018 ◽  
Vol 13 (3) ◽  
pp. 168-179
Author(s):  
Anbo Qiu ◽  
◽  
Zhuowei Wang
Keyword(s):  
Clustering Algorithm ◽  
Density Peak ◽  
Density Peak Clustering
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