Report

Name: Sujing Wang Advisor: Dr. Christoph F. Eick Data Mining & Machine Learning Group Outline 1. Introduction 2. Framework Architecture 3. Methodology 4. Case Study 5. Conclusion and Future Work Data Mining & Machine Learning Sujing Wang 2 Introduction Spatial Data Mining (SDM): 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: Data Mining & Machine Learning Sujing Wang 3 Introduction Spatial objects: point, trajectory(line) polygon(region) Data Mining & Machine Learning Sujing Wang 4 Introduction Challenges: 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 5 A Polygon-based Clustering and Analysis Framework for Mining Spatial Datasets Geospatial Datasets Domain Experts DCONTOUR Notion of Interestingness Spatial Clusters Poly_SNN Reward Functions Meta Clusters Post-processing Summaries and Interesting Patterns 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 ZX1…Xk that maximizes: q(Z ) cZ rewardU (c) subject to: xZ x’Z (xx’ Dist(x,x’)>d) xZ (RewardU(x)>U) xZ x’Z ((x Xi x’ Xk xx’ ) ik) Data Mining & Machine Learning Sujing Wang 7 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 t10 a mean value reward threshold and t21 be a variance reward threshold Interestingness function for each cluster: ( Xi) = max (0, |mv(Xi)| - t1) × max(0, - ((Xi) × t2)) Data Mining & Machine Learning Sujing Wang 8 Case Study 1. Meta-clusters generated from multiple spatial datasets: 30.4 30.2 30.0 Latitude 29.8 29.6 29.4 29.2 29.0 -95.8 -95.6 -95.4 -95.2 -95.0 -94.8 Longitude Data Mining & Machine Learning Sujing Wang 9 Case Study 2. Final Clusters with area of polygons as plug-in reward function 30.4 30.2 80 Latitude 30.0 29.8 150 29.6 21 29.4 125 29.2 13 29.0 -95.8 -95.6 -95.4 -95.2 -95.0 -94.8 Longitude 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. 2 p.m. 12 p.m. Data Mining & Machine Learning Sujing Wang 10 Case Study 3. Finding interesting meta-clusters with respect to solar radiation: 30.2 5 5 29.8 29.6 15 12 edutitaL Latitude 30.0 51 21 29.4 29.2 29.0 -95.8 -95.6 -95.4 Longitude -95.2 -95.0 -94.8 Longitude Cluster ID Mean Variance Number of Polygon 5 15 21 -0.9144 1.1218 1.0184 0.1981 0.1334 0.0350 5 5 3 Data Mining & Machine Learning Sujing Wang 11 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 12 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. 2010. 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 13 Thank you! Data Mining & Machine Learning Sujing Wang 14