1. A Polygon-based Clustering and Analysis Framework for Mining Spatial Dataset
Name: Sujing Wang
Advisor: Dr. Christoph F. Eick
Data Mining & Machine Learning Group

2. Outline Introduction Framework Architecture Methodology Case Study
Conclusion and Future Work
Data Mining & Machine Learning Sujing Wang

3. Introduction Spatial Data Mining (SDM): Spatial object structures:
the process of analyzing and discovering interesting and useful patterns, associations, or relationships from large spatial datasets.
Spatial object structures:
(<spatial attributes>;<non-spatial attributes>)
Example:
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4. Introduction Spatial objects: point, trajectory(line) polygon(region)
Data Mining & Machine Learning Sujing Wang

5. Introduction Challenges: Motivation: Research goal:
Complexity of spatial data types
Spatial relationships
Spatial autocorrelation
Motivation:
Polygons, specially overlapping polygons are very important for mining spatial datasets.
Traditional Clustering algorithms do not work for spatial polygons.
Research goal:
Develop new distance functions and new spatial clustering algorithms for polygons clustering.
Implement novel post-clustering techniques with plug-in reward functions to capture domain experts notation of interestingness.
Data Mining & Machine Learning Sujing Wang

6. A Polygon-based Clustering and Analysis Framework for Mining Spatial Datasets
Geospatial Datasets
Reward Functions
Spatial Clusters
Poly_SNN
Post-processing
Domain Experts
Notion of Interestingness
DCONTOUR
Meta Clusters
Summaries and Interesting Patterns

7. Methodology 1. Domain Driven Final Clustering Generation Methodology
Inputs:
A meta-clustering M={X1, …, Xk} —at most one object will be selected from each meta-cluster Xi (i=1,...k).
The user provides the individual cluster reward function RewardU whose values are in [0,).
A reward threshold U —clusters with low rewards are not included in the final clusterings.
A cluster distance threshold d, which expresses to what extent the user would like to tolerate cluster overlap.
A cluster distance function dist.
Find ZX1…Xk that maximizes:
subject to:
 xZ x’Z (xx’  Dist(x,x’)>d)
 xZ (RewardU(x)>U)
 xZ x’Z ((x Xi  x’ Xk  xx’ )  ik)
Data Mining & Machine Learning Sujing Wang

8. Methodology
2. Finding interesting clusters with respect to continuous non spatial variable V: Let Xi 2A be a cluster in the A-space  be the variance of v with respect in dataset D (Xi) be the variance of variable v in a cluster Xi mv(Xi) the mean value of variable v in a cluster Xi t10 a mean value reward threshold and t21 be a variance reward threshold Interestingness function  for each cluster: ( Xi) = max (0, |mv(Xi)| - t1) × max(0, - ((Xi) × t2))
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9. Case Study 1. Meta-clusters generated from multiple spatial datasets:
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10. Case Study
2. Final Clusters with area of polygons as plug-in reward function
Polygon ID 13 21 80
125
150
Temperature (oF)
79.0
86.35
89.10
84.10
88.87
Solar Radiation (Langleys per minute)
N/A
1.33
1.17
0.13
1.10
Wind Speed (Miles per hour)
4.50
6.10
6.20
4.90
5.39
Time of Day
6 p.m.
1 p.m.
2 p.m.
12 p.m.
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11. 3. Finding interesting meta-clusters with respect to solar radiation:
Case Study
3. Finding interesting meta-clusters with respect to solar radiation:
Cluster ID
Mean
Variance
Number of Polygon 5
0.1981 15
1.1218
0.1334 21
1.0184
0.0350 3
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12. Conclusion & future work
Conclusions:
Our framework can effectively cluster spatial overlapping polygons similar in size, shape and locations.
Our post-clustering techniques with different plug-in reward functions can guide the knowledge extraction of interesting patterns and generate summaries from large spatial datasets.
Future Works:
Develop novel spatial-temporal clustering techniques and embed them to our framework.
Investigating novel change analysis techniques to identify spatial and temporal changes of spatial data.
Evaluate our framework in challenging case studies.
Data Mining & Machine Learning Sujing Wang

13. Publication:
S. Wang, C.S. Chen, V. Rinsourongkawong, F. Akdag, C.F. Eick, “Polygon-based Methodology for Mining Related Spatial Datasets”, ACM SIGSPATIAL GIS Workshop on Data Mining for Geoinformatics (DMG) in conjunction with ACM SIGSPATIAL GIS 2010, San Jose, CA, Nov
NSF travel Award for ACM GIS 2010
 S. Wang, C. Eick, Q. Xu, “A Space-Time Analysis Framework for Mining Geospatial Datasets”, CyberGIS’12 the First International Conference on Space, Time, and CyberGIS, University of Illinois at Urbana-Champaign, Champaign, IL Aug 6-9, 2012.
NSF travel Award for CyberGIS 2012
C. Eick, G. Forestier, S. Wang, Z. Cao, S. Goyal, “A Methodology for Finding Uniform Regions in Spatial Data”, CyberGIS’12 the First International Conference on Space, Time, and CyberGIS, University of Illinois at Urbana-Champaign, Champaign, IL Aug 6-9, 2012.
S. Wang, C.F. Eick, “A Polygon-based Clustering and Analysis Framework for Mining Spatial Datasets”, Geoinformatica, (Under Review).
Data Mining & Machine Learning Sujing Wang

14. Thank you!
Data Mining & Machine Learning Sujing Wang