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Distinguishing friends from strangers in location-based social networks using co-location
(2018-10-01)
Njoo, Gunarto Sindoro
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Hsu, Kuo-Wei
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Peng, Wen-Chih
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交大名義發表
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National Chiao Tung University
This paper particularly focuses on using the spatiotemporal data in the location-based social networks (LBSNs) to infer the social tie between two users. To do so, we first generate a co-location dataset by simulating the meeting event between users based on the time difference and spatial distance. Subsequently, we extract four key features from the generated dataset: diversity, popularity, duration, and stability. We propose a framework called SCI (Social Connection Inference) that integrates all derived features to distinguish real friends' meetings from strangers' coincidental meetings. Experiment results based on the three LBSN datasets prove the effectiveness of the proposed SCI framework by outperforming the state-of-the-art methods. In addition, various discussions on different aspects of the data are presented in this paper to yield insights into using generated co-location datasets. (C) 2018 Elsevier B.V. All rights reserved.
Mining and clustering mobility evolution patterns from social media for urban informatics
(2016-05)
Chen, Chien-Cheng
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Chiang, Meng-Fen
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Peng, Wen-Chih
;
交大名義發表
;
National Chiao Tung University
In this paper, given a set of check-in data, we aim at discovering representative daily movement behavior of users in a city. For example, daily movement behavior on a weekday may show users moving from one to another spatial region associated with time information. Since check-in data contain both spatial and temporal information, we propose a mobility evolution pattern to capture the daily movement behavior of users in a city. Furthermore, given a set of daily mobility evolution patterns, we formulate their similarity distances and then discover representative mobility evolution patterns via the clustering process. Representative mobility evolution patterns are able to infer major movement behavior in a city, which could bring some valuable knowledge for urban planning. Specifically, mobility evolution patterns consist of segments with the spatial region distribution and the corresponding time interval. To measure good segmentation from a set of check-in data, we formulate the problem of mining evolution patterns as a compression problem. In particular, we compute the representation length of the patterns based on the Minimum Description Length principle. Since the number of daily mobility evolution patterns is huge, we further cluster the daily mobility evolution patterns into groups and discover representative patterns. Note that we use the concept of locality-sensitive hashing to accelerate the cluster performance. To evaluate our proposed algorithms, we conducted experiments on the Gowalla and Brightkite datasets, and the experimental results show the effectiveness and efficiency of our proposed algorithms.
Mining Temporal Patterns in Time Interval-Based Data
(2015-12-01)
Chen, Yi-Cheng
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Peng, Wen-Chih
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Lee, Suh-Yin
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資訊工程學系
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Department of Computer Science
Sequential pattern mining is an important subfield in data mining. Recently, applications using time interval-based event data have attracted considerable efforts in discovering patterns from events that persist for some duration. Since the relationship between two intervals is intrinsically complex, how to effectively and efficiently mine interval-based sequences is a challenging issue. In this paper, two novel representations, endpoint representation and endtime representation, are proposed to simplify the processing of complex relationships among event intervals. Based on the proposed representations, three types of interval-based patterns: temporal pattern, occurrence-probabilistic temporal pattern, and duration-probabilistic temporal pattern, are defined. In addition, we develop two novel algorithms, Temporal Pattern Miner (TPMiner) and Probabilistic Temporal Pattern Miner (P-TPMiner), to discover three types of interval-based sequential patterns. We also propose three pruning techniques to further reduce the search space of the mining process. Experimental studies show that both algorithms are able to find three types of patterns efficiently. Furthermore, we apply proposed algorithms to real datasets to demonstrate the effectiveness and validate the practicability of proposed patterns.
Discovering pattern-aware routes from trajectories
(2015-06-01)
Wei, Ling-Yin
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Chang, Kai-Ping
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Peng, Wen-Chih
;
交大名義發表
;
National Chiao Tung University
With the prevalence of GPS-equipped devices and navigation services, users can record and share their driving movements via trajectories. These trajectories reveal users\' driving behaviors for planning routes. In this paper, we propose a novel pattern-aware route discovery framework that considers users\' preferred routes. The proposed framework is comprised of two components: pattern-aware road map generation and route planning. In the first component, we mine significant road segments from historical trajectories, and generate a pattern-aware road map. We design a route score function that strikes a balance between user preference degrees and the length of the route. For the second component, given a source, a destination, and a user pre-defined value k, we intend to derive the top-k routes that consist of road segments from the source to the destination in the pattern-aware road map. To support on-line route planning in most navigation services, we propose a constrained breadth-first-search (CBFS) algorithm. We evaluate the performance of our framework using real trajectory data, and compare our framework with an existing approach in terms of effectiveness and efficiency. The experimental results demonstrate the effectiveness and efficiency of our proposed framework.
A regression-based approach for mining user movement patterns from random sample data
(2011-01-01)
Hung, Chih-Chieh
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Peng, Wen-Chih
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資訊工程學系
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Department of Computer Science
Mobile computing systems usually express a user movement trajectory as a sequence of areas that capture the user movement trace. Given a set of user movement trajectories, user movement patterns refer to the sequences of areas through which a user frequently travels. In an attempt to obtain user movement patterns for mobile applications, prior studies explore the problem of mining user movement patterns from the movement logs of mobile users. These movement logs generate a data record whenever a mobile user crosses base station coverage areas. However, this type of movement log does not exist in the system and thus generates extra overheads. By exploiting an existing log, namely, call detail records, this article proposes a Regression-based approach for mining User Movement Patterns (abbreviated as RUMP). This approach views call detail records as random sample trajectory data, and thus, user movement patterns are represented as movement functions in this article. We propose algorithm LS (standing for Large Sequence) to extract the call detail records that capture frequent user movement behaviors. By exploring the spatio-temporal locality of continuous movements (i.e., a mobile user is likely to be in nearby areas if the time interval between consecutive calls is small), we develop algorithm TC (standing for Time Clustering) to cluster call detail records. Then, by utilizing regression analysis, we develop algorithm MF (standing for Movement Function) to derive movement functions. Experimental studies involving both synthetic and real datasets show that RUMP is able to derive user movement functions close to the frequent movement behaviors of mobile users. (C) 2010 Elsevier B.V. All rights reserved.
An incremental algorithm for clustering spatial data streams: exploring temporal locality
(2013-11-01)
Wei, Ling-Yin
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Peng, Wen-Chih
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資訊工程學系
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Department of Computer Science
Clustering sensor data discovers useful information hidden in sensor networks. In sensor networks, a sensor has two types of attributes: a geographic attribute (i.e, its spatial location) and non-geographic attributes (e.g., sensed readings). Sensor data are periodically collected and viewed as spatial data streams, where a spatial data stream consists of a sequence of data points exhibiting attributes in both the geographic and non-geographic domains. Previous studies have developed a dual clustering problem for spatial data by considering similarity-connected relationships in both geographic and non-geographic domains. However, the clustering processes in stream environments are time-sensitive because of frequently updated sensor data. For sensor data, the readings from one sensor are similar for a period, and the readings refer to temporal locality features. Using the temporal locality features of the sensor data, this study proposes an incremental clustering (IC) algorithm to discover clusters efficiently. The IC algorithm comprises two phases: cluster prediction and cluster refinement. The first phase estimates the probability of two sensors belonging to a cluster from the previous clustering results. According to the estimation, a coarse clustering result is derived. The cluster refinement phase then refines the coarse result. This study evaluates the performance of the IC algorithm using synthetic and real datasets. Experimental results show that the IC algorithm outperforms exiting approaches confirming the scalability of the IC algorithm. In addition, the effect of temporal locality features on the IC algorithm is analyzed and thoroughly examined in the experiments.
Mining sequential patterns across multiple sequence databases
(2009-10-01)
Peng, Wen-Chih
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Liao, Zhung-Xun
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資訊工程學系
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Department of Computer Science
In this paper, given a set of sequence databases across multiple domains, we aim at mining multi-domain sequential patterns, where a multi-domain sequential pattern is a sequence of events whose occurrence time is within a pre-defined time window. We first: propose algorithm Naive in which multiple sequence databases are joined as one sequence database for utilizing traditional sequential pattern mining algorithms (e.g., PrefixSpan). Due to the nature of join operations, algorithm Naive is costly and is developed for comparison purposes. Thus, we propose two algorithms without any join operations for mining multidomain sequential patterns. Explicitly, algorithm IndividualMine derives sequential patterns in each domain and then iteratively combines sequential patterns among sequence databases of multiple domains to derive candidate multi-domain sequential patterns. However, not all sequential patterns mined in the sequence database of each domain are able to form multi-domain sequential patterns. To avoid the mining cost incurred in algorithm IndividualMine, algorithm PropagatedMine is developed. Algorithm PropagatedMine first performs one sequential pattern mining from one sequence database. In light of sequential patterns mined, algorithm PropagatedMine propagates sequential patterns mined to other sequence databases. Furthermore, sequential patterns mined are represented as a lattice structure for further reducing the number of sequential patterns to be propagated. In addition. we develop some mechanisms to allow some empty sets in multi-domain sequential patterns. Performance of the proposed algorithms is comparatively analyzed and sensitivity analysis is conducted. Experimental results show that by exploring propagation and lattice structures, algorithm PropagatedMine outperforms algorithm IndividualMine in terms of efficiency (i.e., the execution time). (C) 2009 Elsevier B.V. All rights reserved.

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