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. NhatHai Phan CIS Department, University of Oregon, Eugene, OR

1. Mining Fuzzy Moving Object Clusters
Phan Nhat Hai, Dino Ienco
Pascal Poncelet, Maguelonne Teissiere
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2. Outline
Background and Motivation
Fuzzy-Closed Swarm Definition
f-CS Miner Algorithm
Experimental Results
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3. Trajectory Data
Spatio-Temporal Data.
Represented as a set of points, located in
space and time.
T=(x1,y1, t1), …, (xn, yn, tn)  position in space
at time ti was (xi, yi).
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4. Mining Movement Patterns
Clustering:
Group together similar trajectories.
Consecutive timestamps:
Flock [GIS 2006], moving cluster [TKDE 2007], convoy
[PVLDB 2008, SSDBM 2010], k-Star [SDM 2009]
Non-consecutive timestamps:
Closed swarm [PVLDB 2010]
rGpattern [GIS 2012]
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5. Motivation
Completely relaxing consecutive time
constraint:
Generate a large number of patterns
Many of them are extraneous
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6. Motivation
Propose:
Fuzzy time gap
Fuzzy time gap participation index
Questions:
Which is relevant time gap?
When the pattern extension should be stopped?
Pattern:
o1 and o2 are moving together from {A>B>C} with
60% weak, 20% medium and 20% strong time
gaps
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7. Outline
Background and Motivation
Fuzzy-Closed Swarm Definition
f-CS Miner Algorithm
Experimental Results
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8. Fuzzy Time Gap
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weak
medium

9. Fuzzy Time Gap Participation Index
},,{},,{ 321 strongmediumweakAxxxX 
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10. f-ClosedSwarm
at least mino objects
at least mint timestamps
Anti-monotonic
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11. f-ClosedSwarm
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12. Outline
Background and Motivation
f-Closed Swarm Definition
f-CS Miner Algorithm
Experimental Results
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13. Straight Forward Approach
Extract all closed swarms
ObjectGrowth [PVLDB 2010]
Post-processing to obtain f-closed swarms
Expensive task:
Search space: O(2|ODB| x 2|TDB|)
New data is always available
Execute again and again the algorithms on the
whole database, i.e. including existing data and
new data
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14. f-CS Miner Algorithm
Objects: transactions
Clusters: items
Pattern: a set of items
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15. f-Closed Swarm in Itemset Context
}{ 654321 cccccc 15

16. General System
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17. Outline
Background and Motivation
f-Closed Swarm Definition
f-CS Miner Algorithm
Experimental Results
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18. Datasets
Datasets:
Swainsoni dataset
43 objects, 4225 different timestamps, gathered from
July 1995 to June 1998.
Buffalo dataset
165 buffalos, gathered from year 2000 to year 2006.
Synthetic data: 500 objects - 10,000 timestamps.
Hardest condition
LCM algorithm [ICDM FIMI 2004]
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19. Effectiveness
Convoy Closed swarm f-Closed swarm
http://www.lirmm.fr/~phan/fcsminer.jsp 19

20. Efficiency (Synthetic Data)
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21. TGi Influence
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22. Conclusion and Future Directions
Propose concept of f-Closed swarm
Propose f-CS Miner
Demonstration system
Extension directions:
Mining top-K representative f-closed swarms
Apply on other patterns, i.e. rGpattern [GIS
2012]
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23. THANK YOU FOR YOUR ATTENTION 
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nhat-hai.phan@teledetection.fr
dino.ienco@teledetection.fr
pascal.poncelet@lirmm.fr
maguelonne.teisseire@teledetection.fr
Demo website: http://www.lirmm.fr/~phan/fcsminer.jsp