An Improved DBSCAN Algorithm to Detect Stops in Individual Trajectories

Abstract

Abstract:With the increasing use of mobile GPS (global positioning system) devices, a large volume of trajectory data on users can be produced. In most existing work, trajectories are usually divided into a set of stops and moves. In trajectories, stops represent the most important and meaningful part of the trajectory; there are many data mining methods to extract these locations. DBSCAN (density-based spatial clustering of applications with noise) is a classical density-based algorithm used to find the high-density areas in space, and different derivative methods of this algorithm have been proposed to find the stops in trajectories. However, most of these methods required a manually-set threshold, such as the speed threshold, for each feature variable. In our research, we first defined our new concept of move ability. Second, by introducing the theory of data fields and by taking our new concept of move ability into consideration, we constructed a new, comprehensive, hybrid feature-based, density measurement method which considers temporal and spatial properties. Finally, an improved DBSCAN algorithm was proposed using our new density measurement method. In the Experimental Section, the effectiveness and efficiency of our method is validated against real datasets. When comparing our algorithm with the classical density-based clustering algorithms, our experimental results show the efficiency of the proposed method.

Continuously tracking of moving object by a combination of ultra-high frequency radio-frequency identification and laser range finder

“…The laser ranging data are segmented into different clusters using DBSCAN. 49 The two closest clusters at the adjacent time are regarded as the same obstacle, and the distance between the two clusters is used to estimate the laser-based velocity. Second, we perform velocity matching to compute the similarity between phase-based velocity and laser-based velocity.…”

The Clusterization of Geo-Tagged Data for Finding City Sights with Use of a Modification of k-MXT Algorithm

“…Empirical results presented in this paper are close to the experimental results obtained in the works [1] and [3] in which it was shown that the k-MXT algorithm is applicable to the solution of the clusterization problem and produces clusters which are comparable to the clusters obtained by DBSCAN algorithm. An extended version of DBSCAN algorithm was examined in [4] to detect stops in individual trajectories using GPS data. The paper [5] proposes a novel niche genetic algorithm (NGA) with density and noise for K-means clustering and apply the algorithm on taxi GPS data sets.…”

A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

“…Issues concerning how to mine the hidden information and understand the meaning of these states, as well as how the information of trajectories can be employed in urban development have become research hotspots. Therefore, a great quantity of clustering-based approaches was presented to describe states of the city, which utilized characteristics and trajectory pattern clustering of GPS data [1,6,[9][10][11][12][13][14][15][16]. For example, Reference [10] presented a density-based line segments trajectories clustering algorithm that was based on a partition and group framework.…”

“…Reference [11] presented a scalable and fast density clustering algorithm that was based on big data computing. Also, the authors of Reference [16] presented an improved density-based algorithm that was to be used for stops clustering in trajectories. In particular, work in Reference [17] proposed an anisotropic (angle-based standard deviation) density-based clustering algorithm, which was used to discover spatial point patterns with noise.…”

“…Furthermore, each of these categories contains several well-known clustering algorithms (e.g., partitioning-based K-means, K-median, and fuzzy C-means (FCM)), with their specific pros and cons. In particular, density-based clustering algorithms are usually used to mine the hidden information of a given dataset and handle any GPS datasets, as they are particularly suitable for discovering clusters with arbitrary shapes and finding mutual exclusion clusters [9][10][11]16,17]. However, it is difficult to handle the overlapping clusters (e.g., trajectory crossover), when considering fuzzy clusters and the loss of local information of trajectories.…”