TY - GEN
T1 - Mining fuzzy moving object clusters
AU - Hai, Phan Nhat
AU - Ienco, Dino
AU - Poncelet, Pascal
AU - Teisseire, Maguelonne
PY - 2012
Y1 - 2012
N2 - Recent improvements in positioning technology have led to a much wider availability of massive moving object data. One of the objectives of spatio-temporal data mining is to analyze such datasets to exploit moving objects that travel together. Naturally, the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps. Thus, there are time gaps among moving object clusters. Existing approaches either put a strong constraint (i.e. no time gap) or completely relaxed (i.e. whatever the time gaps) in dealing with the gaps may result in the loss of interesting patterns or the extraction of huge amount of extraneous patterns. Thus it is difficult for analysts to understand the object movement behavior. Motivated by this issue, we propose the concept of fuzzy swarm which softens the time gap constraint. The goal of our paper is to find all non-redundant fuzzy swarms, namely fuzzy closed swarm. As a contribution, we propose fCS-Miner algorithm which enables us to efficiently extract all the fuzzy closed swarms. Conducted experiments on real and large synthetic datasets demonstrate the effectiveness, parameter sensitiveness and efficiency of our methods.
AB - Recent improvements in positioning technology have led to a much wider availability of massive moving object data. One of the objectives of spatio-temporal data mining is to analyze such datasets to exploit moving objects that travel together. Naturally, the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps. Thus, there are time gaps among moving object clusters. Existing approaches either put a strong constraint (i.e. no time gap) or completely relaxed (i.e. whatever the time gaps) in dealing with the gaps may result in the loss of interesting patterns or the extraction of huge amount of extraneous patterns. Thus it is difficult for analysts to understand the object movement behavior. Motivated by this issue, we propose the concept of fuzzy swarm which softens the time gap constraint. The goal of our paper is to find all non-redundant fuzzy swarms, namely fuzzy closed swarm. As a contribution, we propose fCS-Miner algorithm which enables us to efficiently extract all the fuzzy closed swarms. Conducted experiments on real and large synthetic datasets demonstrate the effectiveness, parameter sensitiveness and efficiency of our methods.
KW - Frequent itemset
KW - Fuzzy closed swarm
KW - Fuzzy time gap
UR - http://www.scopus.com/inward/record.url?scp=84872706115&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872706115&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-35527-1_9
DO - 10.1007/978-3-642-35527-1_9
M3 - Conference contribution
AN - SCOPUS:84872706115
SN - 9783642355264
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 100
EP - 114
BT - Advanced Data Mining and Applications - 8th International Conference, ADMA 2012, Proceedings
T2 - 8th International Conference on Advanced Data Mining and Applications, ADMA 2012
Y2 - 15 December 2012 through 18 December 2012
ER -