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Mining Frequent Spatio-temporal Sequential Patterns

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Presentation on theme: "Mining Frequent Spatio-temporal Sequential Patterns"— Presentation transcript:

1 Mining Frequent Spatio-temporal Sequential Patterns
Authors: Huiping Cao, Nikos Mamoulis, David W. Cheung Department of Computer Science, University of Hong Kong Accepted to ICDM 2005 Abhinaya Sinha Vijay Gandhi CSCI 8715 Date: 4/3/06

2 Outline Introduction Problem Definition Contribution Approach
Experimental Evaluation Assumptions Rewrite today

3 Spatio-Temporal Pattern
Spatio-temporal sequence is a list of time-sampled locations. (x1,y1,t1), (x2,y2,t2),…,(xn,yn,tn) A pattern is a subset of the sequence A pattern is frequent if its support >= threshold value Bus routes across 3 runs

4 Motivation Predict trajectory of an object
Detect frequent routes followed by an object Ecology CHIMPS project at UofM ( Tracking chimpanzee movements and analyzing their behavior Location based services Where a user may go next and thus, provide customized services

5 Problem Definition Input: Given spatio-temporal sequence S and support threshold min_sup Output: Frequent spatio-temporal patterns Objective: Lower computation time and space Constraints: Use of a device such as GPS to sample locations

6 Contributions Model for spatio-temporal sequential pattern mining
Algorithm for extracting frequent singular spatial pattern elements a.k.a. spatial regions Algorithm for combining single regions to patterns of longer length

7 Methodology Convert spatio-temporal sequence to set of line segments
i.e. abstract the trajectory Detect frequent, singular regions Combine singular patterns to longer sequences

8 Modeling the problem Aim: abstract (approximate) the trajectory
Input: original spatio-temporal sequence Output: sequence segments Need locations are not repeated exactly Derived line segments are used for defining spatial regions Compress original data and decrease effort required in mining Uses Douglas-Pecker (DP) algorithm for performing this step

9 Concepts in Modeling Spatio-temporal Segment
Representative line segment of a segment Similarity between line segments | l1.angle – l2.angle | <= θ | l1.len – l2.len | <= length factor * max ( l1.len, l2.len) Closeness between line segments Distance (points belonging to line1, line2) <= ε Mean line segment of a set of line segments Spatial pattern element/ spatial region Sides determined by mean line segment and average orthogonal distance Spatio-temporal sequential pattern Ordered sequence of spatial pattern elements Support of a pattern number of sequences supporting it

10 Discovering frequent singular patterns
Input: All segments representing the spatio-temporal sequence, support threshold Output: All segments have been assigned to regions or been found as outliers. This step constructs frequent single spatial regions Uses similarity and closeness to assign segments to regions

11 Deriving longer patterns
Goal is to derive longer patterns from the singular frequent patterns obtained in previous step. Input is series SR of spatial regions. Output: Frequent patterns. Two algorithms Level-wise mining Mining using substring tree

12 Example Input: r1-r2-r1-r2-r3 minsup = 2 Output candidates
r1-r2 #2; r2-r1 #1; r2-r3 #1 r # r2 #2; r3 #1 Frequent Patterns: r1-r2; r1; r2

13 Level-wise pattern mining
Apriori Algorithm e.g. input: r1-r2-r1-r2 r1(2) r2(2) r1-r2(2) r2-r1(1) Output: r1; r2; r1-r2

14 Contribution: Improvements
Connectivity constraint Closeness property Input: r1-r2-r1-r3-r1-r2 minsup: 2 r1(3) r2(2) r3(1) r1-r2 r2-r1 r1-r3 r3-r1 r2r3 r3r2

15 Mining using substring tree
Tree structure with each node is pattern element The substring tree is used to help in counting long substrings Depth first search gives substring e.g. r1-r2-r1-r3-r1-r2

16 Validation Methodology
Computer simulations Candidate Algorithms: Level-wise, Grid I, Grid II, Substring tree Evaluation done on Real-world dataset Synthetic dataset

17 Experimental Evaluation
Substring takes least time

18 Experimental Evaluation
Extracted patterns Comparison with grid-based approach

19 Assumptions Spatio-temporal sequence is a list of (x,y) values
Trajectory is abstracted by line segments Closeness and similarity of line segments is defined using 2-D data constructs Hence is only applicable to 2-D data

20 Rewrite Today Experimental evaluation on more real-life datasets
Account for noise in input data (inaccuracy of GPS data) Explore techniques that abstract the spatial trajectory with higher accuracy

21 Any questions??

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