Authors: Yu, Qingying | Luo, Yonglong | Chen, Chuanming | Bian, Weixin
Article Type: Research Article
Abstract: Outlier detection is an interesting issue in data mining and machine learning. In this paper, to detect outliers, an information-entropy-based k-nearest neighborhood relevant outlier factor algorithm is proposed that is combined with Shannon information theory and the triangle pruning strategy. The algorithm accounts for the data points whose k-nearest neighbors are distributed on the edge of the range within the designated radius. In particular, the neighborhood influence on each point is considered to address the problem of information concealment and submergence. Information entropy is used to calculate the weights to distinguish the importance of each attribute. Then, based on the …attribute weights, the improved pruning strategy reduces the computational complexity of the subsequent procedures by removing some inliers and obtaining the outlier candidate dataset. Finally, according to the weighted distance between the objects in the candidate dataset and those in the original dataset, the algorithm calculates the dissimilarity between each object and its k-nearest neighbors. The data points with the top $r$ dissimilarity are regarded as the outliers. Experimental results show that, compared to existing methods, the proposed approach improves pruning and detection rates while maintaining the coverage rate. Show more
Keywords: Outlier detection, information entropy, attribute weights, pruning, k-nearest neighborhood relevant outlier factor (kNNROF)
DOI: 10.3233/IDA-150301
Citation: Intelligent Data Analysis, vol. 20, no. 6, pp. 1247-1265, 2016
Authors: Chen, Chuanming | Luo, Yonglong | Yu, Qingying | Hu, Guiyin
Article Type: Research Article
Abstract: The issue of privacy preservation is receiving more and more attention when publishing trajectory data. In this paper, we study the challenges of published trajectory data anonymization. Most existing anonymization methods directly delete the trajectories or locations violating specific constraints, it is likely to cause a large loss of information. To address the problem, this paper proposes a trajectory privacy preservation method based on 3D-Grid partition in order to reduce information loss in the process of trajectory anonymization. This method first divides the trajectory region into several spatio-temporal units (denoted as 3D-cells), and then conducts location exchange or suppression in …each spatio-temporal unit. Based on the trajectory data partition, within each 3D-cell, the proposed method exchanges locations among trajectories or removes very few locations of some sub-trajectories which do not meet the conditions rather than the whole trajectory. Our method considers three scenarios of trajectory distribution and measures trajectory similarity based on time, orientation, spatial locations and other features of trajectory. After the reconstruction of the related anonymous sub-trajectories, an anonymized trajectory dataset is obtained. Theoretical analysis and experimental results show that, compared to other methods, the proposed algorithm effectively preserves trajectory data privacy and improves the anonymous results of trajectory data in terms of accuracy and availability. Show more
Keywords: Privacy preservation, trajectory data partition, 3D-cell, trajectory similarity measurement, trajectory anonymization, trajectory reconstruction
DOI: 10.3233/IDA-183918
Citation: Intelligent Data Analysis, vol. 23, no. 3, pp. 503-533, 2019
Authors: Chen, Chuanming | Zhang, Shuanggui | Yu, Qingying | Ye, Zitong | Ye, Zhen | Hu, Fan
Article Type: Research Article
Abstract: The analysis of user trajectory information and social relationships in social media, combined with the personalization of travel needs, allows users to better plan their travel routes. However, existing methods take only local factors into account, which results in a lack of pertinence and accuracy for the recommended route. In this study, we propose a method by which user clustering, improved genetic, and rectangular region path planning algorithms are combined to design personalized travel routes for users. First, the social relationships of users are analyzed, and close friends are clustered into categories to obtain several friend clusters. Next, the historical …trajectory data of users in the cluster are analyzed to obtain joint points in the trajectory map, these are matched according to the keywords entered by users. Finally, the search area is narrowed and the recommended travel route is obtained through improved genetic and rectangular region path planning algorithms. Theoretical analyses and experimental evaluations show that the proposed method is more accurate at path prediction and regional coverage than other methods. In particular, the average area coverage rate of the proposed method is better than that of the existing algorithm, with a maximum increasement ratio of 31.80%. Show more
Keywords: Tourism route, genetic algorithm, personalized recommendation, route planning
DOI: 10.3233/JIFS-201218
Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 3, pp. 4407-4423, 2021
Authors: Yu, Qingying | Yang, Feng | Xiao, Zhenxing | Gong, Shan | Sun, Liping | Chen, Chuanming
Article Type: Research Article
Abstract: Fast-developing mobile location-aware services generate an enormous volume of trajectory data while adding value to people’s lives. However, trajectory data contains not only location information, but also sensitive personal information. If the original trajectory data is published directly, it could result in serious privacy leaks. Most of the existing privacy-preserving trajectory publishing methods only protect the location information or set the same privacy preservation levels for all moving objects. To meet the users’ personalized privacy requirements and ensure the utility of trajectory location and sensitive information, we propose a trajectory personalized privacy preservation method based on multi-sensitivity attribute generalization and …local suppression. First, we set different security levels for each trajectory by calculating the correlation between sensitive attributes to establish a sensitive attribute classification tree. Second, we generalized sensitive attributes based on privacy preservation levels for each trajectory, the trajectory data still at risk of privacy leakage after generalization was locally suppressed. Finally, an anonymized trajectory dataset was generated. Experimental results on real datasets demonstrated that our method could improve data availability while preserving privacy. Show more
Keywords: Trajectory data publishing, privacy preservation, sensitive attribute generalization, trajectory local suppression, correlation
DOI: 10.3233/IDA-226892
Citation: Intelligent Data Analysis, vol. 27, no. 4, pp. 935-957, 2023
Authors: Chen, Chuanming | Ye, Zhen | Hu, Fan | Gong, Shan | Sun, Liping | Yu, Qingying
Article Type: Research Article
Abstract: Existing trajectory-clustering methods do not consider road-network connectivity, road directionality, and real path length while measuring the similarity between different road-network trajectories. This paper proposes a trajectory-clustering method based on road-network-sensitive features, which can solve the problem of similarity metrics among trajectories in the road network, and effectively combine their local and overall similarity features. First, the method performs the primary clustering of trajectories based on the overall vehicle motion trends. Then, the map-matched trajectories are clustered based on the road segment density, connectivity, and corner characteristics. Finally, clustering is then merged based on the multi-area similarity measure. The visualization …and experimental results on real road-network trajectories show that the proposed method is more effective and comprehensive than existing methods, and more suitable for urban road planning, public transportation planning, and congested road detection. Show more
Keywords: Trajectory clustering, map matching, road-network trajectory, trajectory similarity
DOI: 10.3233/JIFS-211270
Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 2357-2375, 2021
Authors: Chen, Chuanming | Lin, Wenshi | Zhang, Shuanggui | Ye, Zitong | Yu, Qingying | Luo, Yonglong
Article Type: Research Article
Abstract: Trajectory data may include the user’s occupation, medical records, and other similar information. However, attackers can use specific background knowledge to analyze published trajectory data and access a user’s private information. Different users have different requirements regarding the anonymity of sensitive information. To satisfy personalized privacy protection requirements and minimize data loss, we propose a novel trajectory privacy preservation method based on sensitive attribute generalization and trajectory perturbation. The proposed method can prevent an attacker who has a large amount of background knowledge and has exchanged information with other attackers from stealing private user information. First, a trajectory dataset is …clustered and frequent patterns are mined according to the clustering results. Thereafter, the sensitive attributes found within the frequent patterns are generalized according to the user requirements. Finally, the trajectory locations are perturbed to achieve trajectory privacy protection. The results of theoretical analyses and experimental evaluations demonstrate the effectiveness of the proposed method in preserving personalized privacy in published trajectory data. Show more
Keywords: Trajectory data publication, personalized privacy preservation, sensitive attribute generalization, location perturbation
DOI: 10.3233/IDA-205306
Citation: Intelligent Data Analysis, vol. 25, no. 5, pp. 1247-1271, 2021
Authors: Yu, Qingying | Xiao, Zhenxing | Yang, Feng | Gong, Shan | Shi, Gege | Chen, Chuanming
Article Type: Research Article
Abstract: With the continuous expansion of city scale and the advancement of transportation technology, route recommendations have become an increasingly common concern in academic and engineering circles. Research on route recommendation technology can significantly satisfy the travel demands of residents and city operations, thereby promoting the construction of smart cities and the development of intelligent transportation. However, most current route recommendation methods focus on generating a route satisfying a single objective attribute and fail to comprehensively consider other types of objective attributes or user preferences to generate personalized recommendation routes. This study proposes a multi-objective route recommendation method based on the …reinforcement learning algorithm Q-learning, that comprehensively considers multiple objective attributes, such as travel time, safety risk, and COVID-19 risk, and generates recommended routes that satisfy the requirements of different scenarios by combining user preferences. Simultaneously, to address the problem that the Q-learning algorithm has low iteration efficiency and easily falls into the local optimum, this study introduces the dynamic exploration factor σ and initializes the value function in the road network construction process. The experimental results show that, when compared to other traditional route recommendation algorithms, the recommended path generated by the proposed algorithm has a lower path cost, and based on its unique Q -value table search mechanism, the proposed algorithm can generate the recommended route almost in real time. Show more
Keywords: Route recommendation, multi-objective, user preferences, reinforcement learning, dynamic exploration factor
DOI: 10.3233/JIFS-222932
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 4, pp. 7009-7025, 2023
Authors: Xu, Dongsheng | Chen, Chuanming | Jin, Qi | Zheng, Ming | Ni, Tianjiao | Yu, Qingying
Article Type: Research Article
Abstract: Abnormal-trajectory detection can be used to detect fraudulent behavior of taxi drivers transporting passengers. Aiming at the problem that existing methods do not fully consider abnormal fragments of trajectories, this paper proposes an abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition, which effectively detects abnormal sub-trajectories and further detects abnormal trajectories. First, each trajectory is reconstructed using the turning angles and is divided into multiple sub-trajectories according to the turning angle threshold and trajectory point original acceleration. The sub-trajectories are then classified according to the extracted directional features. Finally, the multivariate distances between angular adjacent segments are calculated …to obtain the outlier factor, and abnormal sub-trajectories are detected. The sum of the lengths of the abnormal sub-trajectories is used to calculate the outlier score and identify abnormal trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods. Show more
Keywords: Abnormal-trajectory detection, trajectory reconstruction, directional feature, outlier factor, sub-trajectory classification
DOI: 10.3233/JIFS-236508
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 4, pp. 8477-8496, 2024
Authors: Chen, Chuanming | Xu, Dongsheng | Jin, Qi | Wang, Wenkai | Sun, Liping | Zheng, Xiaoyao | Yu, Qingying
Article Type: Research Article
Abstract: Trajectory-outlier detection can be used to discover the fraudulent behaviour of taxi drivers during operations. Existing detection methods typically consider each trajectory as a whole, resulting in low accuracy and slow speed. In this study, a trajectory outlier detection method based on group division is proposed. First, the urban vector region is divided into a series of grids of fixed size, and the grid density is calculated based on the urban road network. Second, according to the grid density, the grids were divided into high- and low-density grids, and the code sequence for each trajectory was obtained using grid coding …and density. Third, the trajectory dataset is divided into several groups based on the number of low-density grids through which each trajectory passes. Finally, based on the high-density grid sequences, a regular subtrajectory dataset was obtained within each trajectory group, which was used to calculate the trajectory deviation to detect outlying trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods. Show more
Keywords: Trajectory outlier detection, group division, grid density, trajectory group, regular sub-trajectories
DOI: 10.3233/IDA-237384
Citation: Intelligent Data Analysis, vol. 28, no. 2, pp. 415-432, 2024