Trajectory Clustering by Sampling and Density

2014 ◽  
Vol 48 (6) ◽  
pp. 74-85 ◽  
Author(s):  
Jiacai Pan ◽  
Qingshan Jiang ◽  
Zheping Shao
Keyword(s):  
Traffic Flow ◽  
Clustering Algorithm ◽  
Moving Objects ◽  
Distance Measure ◽  
Classical Method ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Density Based Clustering ◽  
Clustering Quality ◽  
Parameter Values

AbstractThe trajectory data of moving objects contain huge amounts of information pertaining to traffic flow. It is incredibly important to extract valuable knowledge from this particular kind of data. Trajectory clustering is one of the most widely used approaches to complete this extraction. However, the current practice of trajectory clustering always groups similar subtrajectories that are partitioned from the trajectories; these methods would thus lose important information of the trajectory as a whole. To deal with this problem, this paper introduces a new trajectory-clustering algorithm based on sampling and density, which groups similar traffic movement tracks (car, ship, airplane, etc.) for further analysis of the characteristics of traffic flow. In particular, this paper proposes a novel technique of measuring distances between trajectories using point sampling. This distance measure does not divide the trajectory and thus conserves the integrated knowledge of these trajectories. This trajectory clustering approach is a new adaptation of a density-based clustering algorithm to the trajectories of moving objects. This paper then adopts the entropy theory as the heuristic for selecting the parameter values of this algorithm and the sum of the squared error method for measuring the clustering quality. Experiments on real ship trajectory data have shown that this algorithm is superior to the classical method TRACLUSS in the run time and that this method works well in discovering traffic flow patterns.

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.

An efficient trajectory-clustering algorithm based on an index tree

2011 ◽  
Vol 34 (7) ◽  
pp. 850-861 ◽  
Author(s):  
Guan Yuan ◽  
Shixiong Xia ◽  
Lei Zhang ◽  
Yong Zhou ◽  
Cheng Ji
Keyword(s):  
Radio Frequency Identification ◽  
Clustering Algorithm ◽  
Real Data ◽  
Structural Similarity ◽  
Location Based Services ◽  
Similarity Function ◽  
Data Sets ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Index Tree

With the development of location-based services, such as the Global Positioning System and Radio Frequency Identification, a great deal of trajectory data can be collected. Therefore, how to mine knowledge from these data has become an attractive topic. In this paper, we propose an efficient trajectory-clustering algorithm based on an index tree. Firstly, an index tree is proposed to store trajectories and their similarity matrix, with which trajectories can be retrieved efficiently; secondly, a new conception of trajectory structure is introduced to analyse both the internal and external features of trajectories; then, trajectories are partitioned into trajectory segments according to their corners; furthermore, the similarity between every trajectory segment pairs is compared by presenting the structural similarity function; finally, trajectory segments are grouped into different clusters according to their location in the different levels of the index tree. Experimental results on real data sets demonstrate not only the efficiency and effectiveness of our algorithm, but also the great flexibility that feature sensitivity can be adjusted by different parameters, and the cluster results are more practically significant.

IMPROVED DENSITY BASED ALGORITHM FOR DATA STREAM CLUSTERING

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Maryam Mousavi ◽  
Azuraliza Abu Bakar
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Local Density ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Stream Clustering ◽  
Density Based Clustering ◽  
Clustering Quality ◽  
Data Stream Clustering

In recent years, clustering methods have attracted more attention in analysing and monitoring data streams. Density-based techniques are the remarkable category of clustering techniques that are able to detect the clusters with arbitrary shapes and noises. However, finding the clusters with local density varieties is a difficult task. For handling this problem, in this paper, a new density-based clustering algorithm for data streams is proposed. This algorithm can improve the offline phase of density-based algorithm based on MinPts parameter. The experimental results show that the proposed technique can improve the clustering quality in data streams with different densities.

scholarly journals APDS: A framework for discovering movement pattern from trajectory database

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988816
Author(s):  
Guan Yuan ◽  
Zhongqiu Wang ◽  
Zhixiao Wang ◽  
Fukai Zhang ◽  
Li Yuan ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Moving Objects ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Movement Pattern ◽  
Moving Object ◽  
Periodic Pattern ◽  
Trajectory Data ◽  
Discovery System ◽  
Spatiotemporal Data Mining

Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects’ behavior and analyze their habits. To promote the application of spatiotemporal data mining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.

scholarly journals Aircraft Trajectory Clustering in Terminal Airspace Based on Deep Autoencoder and Gaussian Mixture Model

Aerospace ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 266
Author(s):  
Weili Zeng ◽  
Zhengfeng Xu ◽  
Zhipeng Cai ◽  
Xiao Chu ◽  
Xiaobo Lu
Keyword(s):  
Traffic Flow ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Flow Patterns ◽  
Clustering Methods ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Clustering Method ◽  
Aircraft Trajectory

The aircraft trajectory clustering analysis in the terminal airspace is conducive to determining the representative route structure of the arrival and departure trajectory and extracting their typical patterns, which is important for air traffic management such as airspace structure optimization, trajectory planning, and trajectory prediction. However, the current clustering methods perform poorly due to the large flight traffic, high density, and complex airspace structure in the terminal airspace. In recent years, the continuous development of Deep Learning has demonstrated its powerful ability to extract internal potential features of large dataset. Therefore, this paper mainly tries a deep trajectory clustering method based on deep autoencoder (DAE). To this end, this paper proposes a trajectory clustering method based on deep autoencoder (DAE) and Gaussian mixture model (GMM) to mine the prevailing traffic flow patterns in the terminal airspace. The DAE is trained to extract feature representations from historical high-dimensional trajectory data. Subsequently, the output of DAE is input into GMM for clustering. This paper takes the terminal airspace of Guangzhou Baiyun International Airport in China as a case to verify the proposed method. Through the direct visualization and dimensionality reduction visualization of the clustering results, it is found that the traffic flow patterns identified by the clustering method in this paper are intuitive and separable.

RBF Neural Network (RBFNN) using Density Based Clustering for Liver Disorder Dataset

2017 ◽  
Vol 7 (7) ◽  
pp. 20
Author(s):  
Sunila Godara ◽  
Rishipal Singh ◽  
Sanjeev Kumar
Keyword(s):  
Neural Network ◽  
Common Property ◽  
Clustering Algorithm ◽  
Distance Measure ◽  
Rbf Neural Network ◽  
Unsupervised Classification ◽  
Liver Disorder ◽  
Data Set ◽  
Density Based Clustering

Clustering is an unsupervised classification that is the partitioning of a data set in a set of meaningful subsets. Each object in dataset shares some common property- often proximity according to some defined distance measure. In this paper we will extend our previous work [15]. Simple K-means and Proposed makeDensityBased    Clustering (MDBC) are embedded in RBF Neural Network (RBFNN). We evaluated the performance of  RBFNN using K-Means and Proposed makeDensityBased Clustering on Liver Disorder Dataset. Proposed algorithm is superior to the existing makeDensityBased Clustering algorithm [15], but it is not capable of performing well when it is embedded with RBFNN.

scholarly journals Design of intelligent acquisition system for moving object trajectory data under cloud computing

2021 ◽  
Vol 30 (1) ◽  
pp. 763-773
Author(s):  
Yang Zhang ◽  
Abhinav Asthana ◽  
Sudeep Asthana ◽  
Shaweta Khanna ◽  
Ioan-Cosmin Mihai
Keyword(s):  
Cloud Computing ◽  
Clustering Algorithm ◽  
Programming Model ◽  
Hot Spot ◽  
Moving Object ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Spot Area ◽  
Time Period ◽  
Moving Object Trajectory

Abstract In order to study the intelligent collection system of moving object trajectory data under cloud computing, information useful to passengers and taxi drivers is collected from massive trajectory data. This paper uses cloud computing technology, through clustering algorithm and density-based DBSCAN algorithm combined with Map Reduce programming model and design trajectory clustering algorithm. The results show that based on the 8-day data of 15,000 taxis in Shenzhen, the characteristic time period is determined. The passenger hot spot area is obtained by clustering the passenger load points in each time period, which verifies the feasibility of the passenger load point recommendation application based on trajectory clustering. Therefore, in the absence of holidays, the number of passenger hotspots tends to be stable. It is reliable to perform cluster analysis. The recommended application has been demonstrated through experiments, and the implementation results show the rationality of the recommended application design and the feasibility of practice.

scholarly journals Mining Taxi Pick-Up Hotspots Based on Grid Information Entropy Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Shuoben Bi ◽  
Ruizhuang Xu ◽  
Aili Liu ◽  
Luye Wang ◽  
Lei Wan
Keyword(s):  
Information Entropy ◽  
Input Data ◽  
Clustering Algorithm ◽  
Scientific Basis ◽  
Urban Traffic ◽  
Massive Data ◽  
Trajectory Data ◽  
Research Areas ◽  
Density Based Clustering ◽  
Traffic Guidance

In view of the fact that the density-based clustering algorithm is sensitive to the input data, which results in the limitation of computing space and poor timeliness, a new method is proposed based on grid information entropy clustering algorithm for mining hotspots of taxi passengers. This paper selects representative geographical areas of Nanjing and Beijing as the research areas and uses information entropy and aggregation degree to analyze the distribution of passenger-carrying points. This algorithm uses a grid instead of original trajectory data to calculate and excavate taxi passenger hotspots. Through the comparison and analysis of the data of taxi loading points in Nanjing and Beijing, it is found that the experimental results are consistent with the actual urban passenger hotspots, which verifies the effectiveness of the algorithm. It overcomes the shortcomings of a density-based clustering algorithm that is limited by computing space and poor timeliness, reduces the size of data needed to be processed, and has greater flexibility to process and analyze massive data. The research results can provide an important scientific basis for urban traffic guidance and urban management.

Moving Objects Gathering Patterns Retrieving based on Spatio-Temporal Graph

2016 ◽  
Vol 13 (3) ◽  
pp. 88-107 ◽  
Author(s):  
Junming Zhang ◽  
Jinglin Li ◽  
Zhihan Liu ◽  
Quan Yuan ◽  
Fangchun Yang
Keyword(s):  
Clustering Algorithm ◽  
Moving Objects ◽  
High Volume ◽  
Complete Graphs ◽  
Trajectory Data ◽  
Traffic System ◽  
Spatial Coordinates ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Retrieving Algorithm

Moving objects gathering pattern represents a group events or incidents that involve congregation of moving objects, enabling the analysis of traffic system. However, effectively and efficiently discovering the specific gathering pattern turns to be a remaining challenging issue since the large number of moving objects will generate high volume of trajectory data. In order to address this issue, the authors propose a moving object gathering pattern retrieving method that aims to support the retrieving of gathering patterns based on spatio-temporal graph. In this method, firstly the authors use an improved R-tree based density clustering algorithm (RT-DBScan) to index the moving objects and collect clusters. Then, they maintain a spatio-temporal graph rather than storing the spatial coordinates to obtain the spatio-temporal changes in real time. Finally, a gathering retrieving algorithm is developed by searching the maximal complete graphs which meet the spatio-temporal constraints. To the best of their knowledge, effectiveness and efficiency of the proposed methods are outperformed other methods on both real and large trajectory data.

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