TY - JOUR
T1 - Splitter
T2 - Mining finegrained sequential patterns in semantic trajectories
AU - Zhang, Chao
AU - Han, Jiawei
AU - Shou, Lidan
AU - Lu, Jiajun
AU - Porta, Thomas La
PY - 2014
Y1 - 2014
N2 - Driven by the advance of positioning technology and the popularity of location-sharing services, semantic-enriched trajectory data have become unprecedentedly available. The sequential patterns hidden in such data, when properly defined and extracted, can greatly benefit tasks like targeted advertising and urban planning. Unfortunately, classic sequential pattern mining algorithms developed for transactional data cannot effectively mine patterns in semantic trajectories, mainly because the places in the continuous space cannot be regarded as independent "items". Instead, similar places need to be grouped to collaboratively form frequent sequential patterns. That said, it remains a challenging task to mine what we call fine-grained sequential patterns, which must satisfy spatial compactness, semantic consistency and temporal continuity simultaneously. We propose SPLITTER to effectively mine such fine-grained sequential patterns in two steps. In the first step, it retrieves a set of spatially coarse patterns, each attached with a set of trajectory snippets that precisely record the pattern's occurrences in the database. In the second step, SPLITTER breaks each coarse pattern into fine-grained ones in a top-down manner, by progressively detecting dense and compact clusters in a higher-dimensional space spanned by the snippets. SPLITTER uses an effective algorithm called weighted snippet shift to detect such clusters, and leverages a divide-and-conquer strategy to speed up the top-down pattern splitting process. Our experiments on both real and synthetic data sets demonstrate the effectiveness and efficiency of SPLITTER.
AB - Driven by the advance of positioning technology and the popularity of location-sharing services, semantic-enriched trajectory data have become unprecedentedly available. The sequential patterns hidden in such data, when properly defined and extracted, can greatly benefit tasks like targeted advertising and urban planning. Unfortunately, classic sequential pattern mining algorithms developed for transactional data cannot effectively mine patterns in semantic trajectories, mainly because the places in the continuous space cannot be regarded as independent "items". Instead, similar places need to be grouped to collaboratively form frequent sequential patterns. That said, it remains a challenging task to mine what we call fine-grained sequential patterns, which must satisfy spatial compactness, semantic consistency and temporal continuity simultaneously. We propose SPLITTER to effectively mine such fine-grained sequential patterns in two steps. In the first step, it retrieves a set of spatially coarse patterns, each attached with a set of trajectory snippets that precisely record the pattern's occurrences in the database. In the second step, SPLITTER breaks each coarse pattern into fine-grained ones in a top-down manner, by progressively detecting dense and compact clusters in a higher-dimensional space spanned by the snippets. SPLITTER uses an effective algorithm called weighted snippet shift to detect such clusters, and leverages a divide-and-conquer strategy to speed up the top-down pattern splitting process. Our experiments on both real and synthetic data sets demonstrate the effectiveness and efficiency of SPLITTER.
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U2 - 10.14778/2732939.2732949
DO - 10.14778/2732939.2732949
M3 - Article
AN - SCOPUS:84901768820
VL - 7
SP - 769
EP - 780
JO - Proceedings of the VLDB Endowment
JF - Proceedings of the VLDB Endowment
SN - 2150-8097
IS - 9
ER -