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Clustering Trajectories of Moving Objects in an Uncertain World 1 Dept. of Informatics, Univ. of Piraeus, Greece 2 Tech. Educational Institute of Crete,

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Presentation on theme: "Clustering Trajectories of Moving Objects in an Uncertain World 1 Dept. of Informatics, Univ. of Piraeus, Greece 2 Tech. Educational Institute of Crete,"— Presentation transcript:

1 Clustering Trajectories of Moving Objects in an Uncertain World 1 Dept. of Informatics, Univ. of Piraeus, Greece 2 Tech. Educational Institute of Crete, Greece Nikos Pelekis 1, Ioannis Kopanakis 2, Evangelos E. Kotsifakos 1, Elias Frentzos 1, Yannis Theodoridis 1 IEEE International Conference on Data Mining (ICDM 2009), Miami, FL, USA, 6-9 December, 2009

2 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 2 Outline Related work Motivation Our contribution  From Trajectories to Intuitionistic Fuzzy Sets  A similarity metric for Uncertain Trajectories (Un-Tra)  Cen-Tra: The Centroid Trajectory of a bunch of trajectories  TR-I-FCM: A novel clustering algorithm for Un-Tra Experimental study Conclusions & future work

3 Related Work on Mobility Data Mining Trajectory clustering

4 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 4 Trajectory Clustering Questions:  Which distance between trajectories?  Which kind of clustering?  What is a cluster ‘mean’ or ‘centroid’? A representative trajectory?

5 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 5 Average Euclidean distance “Synchronized” behaviour distance “Synchronized” behaviour distance  Similar objects = almost always in the same place at the same time Computed on the whole trajectory Computed on the whole trajectory Computational aspects: Computational aspects:  1  2   Cost = O( |  1| + |  2| ) (|  | = number of points in  )  It is a metric => efficient indexing methods allowed, e.g. [Frentzos et al. 2007] Timeseries-based approaches: LCSS, DTW, ERP, EDR Trajectory-oriented approach:  (time-relaxed) route similarity vs. (time-aware) trajectory similarity and variations (speed- pattern based similarity; directional similarity; …) [Pelekis et al. 2007] Which distance? distance between moving objects  1 and  2 at time t || ))(),(( |),( T dtttd D T T    

6 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 6 K-means T-OPTICS [Nanni & Pedreschi, 2006] HAC-average Which kind of clustering? Reachability plot (= objects reordering for distance distribution)  threshold

7 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 7 [Lee et al. 2007] Discovers similar portions of trajectories (sub-trajectories) Two phases: partitioning and grouping TRACLUS: A Partition-and-Group Framework

8 What about usage of Mobility Patterns? Visual analytics for mobility data

9 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 9 Visual analytics for mobility data [Andrienko et al. 2007] What is an appropriate way to visualize groups of trajectories?

10 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 10 Summarizing a bunch of trajectories 1) Trajectories  sequences of “moves” between “places” 2) For each pair of “places”, compute the number of “moves” 3) Represent “moves” by arrows (with proportional widths) Major flow Minor variations Many small moves

11 A word on uncertainty

12 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 12 Handling Uncertainty Handling uncertainty is a relatively new topic! A lot of research effort has been assigned  Developing models for representing uncertainty in trajectories. The most popular one [Trajcevski et al. 2004]: a trajectory of an object is modeled as a 3D cylindrical volume around the tracked trajectory (polyline) Various degrees of uncertainty

13 Coming back to our approach

14 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 14 Challenge 1: Introduce trajectory fuzziness in spatial clustering techniques  The application of spatial clustering algorithms (k-means, BIRCH, DBSCAN, STING, …) to Trajectory Databases (TD) is not straightforward  Fuzzy clustering algorithms (Fuzzy C-Means and its variants) quantify the degree of membership of each data vector to a cluster  The inherent uncertainty in TD should taken into account. Challenge 2: study the nature of the centroid / mean / representative trajectory in a cluster of trajectories.  Is it a ‘trajectory’ itself? Motivation

15 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 15 I-Un-Tra: An intuitionistic fuzzy vector representation of trajectories  enables clustering of trajectories by existing (fuzzy or not) clustering algorithms D UnTra : A distance metric of uncertain trajectories Cen-Tra: The centroid of a bunch of trajectories  using density and local similarity properties TR-I-FCM: A novel modification of FCM algorithm for clustering complex trajectory datasets  exploiting on D UnTra and Cen-Tra. Our contribution

16 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 16 From Fuzzy sets to Intuitionistic fuzzy sets Definition 1 (Zadeh, 1965). Let a set E be fixed. A fuzzy set on E is an object of the form Definition 2 (Atanassov, 1986; Atanassov, 1994). An intuitionistic fuzzy set (IFS) A is an object of the form where and where

17 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 17 Hesitancy For every element The hesitancy of the element x to the set A is

18 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 18 Vector representation of trajectories Assume a regular grid G(m  n) consisting of cells c k,l, a trajectory and a target dimension p << n i, The “approximate trajectory” consists of p regions (i.e. sets of cells) crossed by T i during period p j The “Uncertain Trajectory” is the ε- buffer of

19 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 19 Intuitionistic Uncertain Trajectories membership = inside cell with 100% probability (i.e. thick portions) non-membership = outside cell with 100% probability (i.e. dotted portions) hesitancy = ignorance whether inside or outside the cell (i.e. solid thin portions) A cell c k.l c k.l ε

20 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 20 Intuitionistic Uncertain Trajectories

21 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 21 Proposed similarity metric (1/2) The distance between two I-UnTra A and B is: where and

22 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 22 Proposed similarity metric (2/2) Assuming two intuitionistic fuzzy sets on it, A = (M A, Γ A, Π A ) and B = (M B, Γ B, Π Β ), with the same cardinality n, the similarity measure Z between A and B is given by the following equation: where z(A’,B’) for fuzzy sets A' and B' (e.g. for M A, M B ) is defined as: and similarly for Γ A, Γ B and Π A, Π B.

23 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 23 An example A={x, 0.4, 0.2}, B={x, 0.5, 0.3}, C={x, 0.5, 0.2} C is more similar to A than B

24 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 24 The Cen troid Tra jectory The idea (similarity-density-based approach):  adopt some local similarity function to identify common sub-trajectories (concurrent existence in space-time),  follow a region growing approach according to density

25 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 25 T1 T2 T3 Algorithm CenTra: An example

26 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 26 T1 T2 T3 The Cen troid Tra jectory

27 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 27 The FCM objective function: Given that to be minimized requires: and Fuzzy C-Means algorithm 1. Determine c (1 < c < N), and initialize V(0), j=1, 2. Calculate the membership matrix U(j), 3. Update the centroids’ matrix V(j), 4. If |U(j+1)-U(j)|>ε then j=j+1 and go to Step 2.

28 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 28 Ignore update centroid step and instead use CenTra The FCM objective function: Given that to be minimized requires : and CenTR-I-FCM algorithm 1. V(0) = c random I-UnTra; j=1; 2. repeat 3. Calculate membership matrix U(j) 4. Update the centroids’ matrix V(j) using CenTra; 5. Compute membership and non-membership degrees of V(j) 6. Until ||Uj+1-Uj||F≤ε; j=j+1;

29 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 29 Experiments (1/2) Dataset: ’Athens trucks’ MOD (www.rtreeportal.org)  50 trucks, 1100 trajectories, position records

30 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 30 Experiments (2/2) Use CommonGIS [Andrienko et al., 2007] to identify real clusters “Round trips” clusters “Linear” clusters

31 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 31 Results (Clustering accuracy scaling cell size, ε ) Fix density threshold to δ=2% of the total number of trajectories

32 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 32 Results (Clustering accuracy scaling density threshold, δ) Fix uncertainty to ε=1

33 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 33 Results (scaling the number of clusters)

34 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 34 Results (scaling the dataset cardinality)

35 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 35 Results (Quality of CenTra) Representative Trajectories vs. Centroid Trajectories cell size=1.3%, ε=0, δ=0.09 cell size=1.3%, ε=0, δ=0.09, cell size=2.8%, ε=0, δ=0.02

36 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 36 Conclusions We proposed a three-step approach for clustering trajectories of moving objects, motivated by the observation that clustering and representation issues in TD are inherently subject to uncertainty.  1 st step: an intuitionistic fuzzy vector representation of trajectories plus a distance metric consisting of a metric for sequences of regions and a metric for intuitionistic fuzzy sets  2 nd step: Algorithm CenTra, a novel technique for discovering the centroid of a bundle of trajectories  3 rd step: Algorithm CenTR-I-FCM, for clustering trajectories under uncertainty

37 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 37 Future Work Devise a clever sampling technique for multi-dimensional data so as to diminish the effect of initialization in the algorithm; Exploit the metric properties of the proposed distance function by using an distance-based index structure (for efficiency purposes); Perform extensive experimental evaluation using large trajectory datasets

38 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 38 Acknowledgements Research partially supported by the FP7 ICT/FET Project MODAP (Mobility, Data Mining, and Privacy) funded by the European Union. URL: a continuation of the FP IST/FET Project GeoPKDD (Geographic Privacy-aware Knowledge Discovery and Delivery) funded by the European Union. URL: Some slides are from:  Fosca Giannotti, Dino Pedreschi, and Yannis Theodoridis, “Geographic Privacy-aware Knowledge Discovery and Delivery”, EDBT Tutorial, 2009.

39 Back up slides

40 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 40 Examples of mobility patterns exploitation Trajectory Density-based queries  Find hot-spots (popular places) [Giannotti et al. 2007]  Find T-Patterns [Giannotti et al. 2007]  Find hot motion paths [Sacharidis et al. 2008]  Find typical trajectories [Lee et al. 2007]  Identify flocks & leaders [Benkert et al. 2008] δtδt ε X Y T

41 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 41 Which kind of clustering? General requirements:  Non-spherical clusters should be allowed E.g.: A traffic jam along a road = “snake-shaped” cluster  Tolerance to noise  Low computational cost  Applicability to complex, possibly non-vectorial data

42 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 42 Temporal focusing Different time intervals can show different behaviours  E.g.: objects that are close to each other within a time interval can be much distant in other periods of time The time interval becomes a parameter  E.g.: rush hours vs. low traffic times Already supported by the distance measure  1,  2  Just compute D(  1,  2 ) | T on a time interval T’  T Problem: significant T’ are not always known a priori  An automated mechanism is needed to find them

43 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 43 The representative trajectory of the cluster:  Compute the average direction vector and rotate the axes temporarily.  Sort the starting and ending points by the coordinate of the rotated axis.  While scanning the starting and ending points in the sorted order, count the number of line segments and compute the average coordinate of those line segments. TRACLUS – representative trajectory

44 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 44 Trajectory Uncertainty vs. Anonymization Never Walk Alone [Bonchi et al. 2008]  Trade uncertainty for anonymity: trajectories that are close up the uncertainty threshold are indistinguishable  Combine k-anonymity and perturbation Two steps:  Cluster trajectories into groups of k similar ones (removing outliers)  Perturb trajectories in a cluster so that each one is close to each other up to the uncertainty threshold

45 Pelekis et al. "Clustering Trajectories of Moving Objects in an Uncertain World" 45 Qualitative evaluation of Z


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