A clustering-based visualization of colocation patterns Elise Desmier - PowerPoint PPT Presentation

A clustering-based visualization of colocation patterns Elise Desmier 1 , Frédéric Flouvat 2 , Dominique Gay 3 and Nazha Selmaoui-Folcher 2 1 Université de Lyon, LIRIS , UMR5205 CNRS, Villeurbanne, France elise.desmier@liris.cnrs.fr 2 University of New Caledonia, PPME , EA3325, Nouméa, New Caledonia frederic.flouvat@univ-nc.nc nazha.selmaoui@univ-nc.nc 3 TECH/ASAP/PROF, Orange Labs , Lannion, France dominique.gay@orange-ftgroup.com IDEAS’11, Lisboa

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Toward a better visualization of spatial patterns One of the major issues in data mining (Han and Kamber 06) "the presentation and visualization of discovered knowledge expressed in high-level languages, visual representations, or other expressive forms so that the knowledge can be easily understood and directly usable by humans" Problem with existing solutions No solutions to display spatial patterns (colocations) in a simple, concise and intuitive way for experts Contribution A new visualization of colocations based on a heuristic clustering method easily usable and interpretable by domain experts additional spatial and thematic informations wrt "classical" colocations Frédéric Flouvat A clustering-based visualization of colocations 2 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Outline 1 Context 2 Spatial pattern mining and visualization 3 Visualization of colocations 4 Application 5 Conclusion Frédéric Flouvat A clustering-based visualization of colocations 3 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Application Context New Caledonia Exceptional biodiversity and caledonian lagoons declared a World Heritage site by the UNESCO But important mining projects (25% of world resources in Nickel), a tropical climate with cyclones and bush fires Important soil erosion Strong impact on terrestrial and littoral ecosystems ➫ FO.S.T.ER. project (financed by the French government) A multidisciplinary consortium composed of specialists in data mining, image processing and geology Providing to geologists a semi-automatic and complete process for monitoring soil erosion Frédéric Flouvat A clustering-based visualization of colocations 4 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Data Complex data Heterogenous data : DEM, vegetation, soils occupation , climate, ... Large and spatial data ➫ Need of advanced analysis and modelization methods to assist experts Spatial data mining Extracting interesting useful and unexpected knowledge in spatial data A large number of descriptive and/or predictive methods • e.g. spatial decision trees, clustering, spatial pattern mining ... Focus on colocations (spatial patterns) Frédéric Flouvat A clustering-based visualization of colocations 5 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Outline 1 Context 2 Spatial pattern mining and visualization 3 Visualization of colocations 4 Application 5 Conclusion Frédéric Flouvat A clustering-based visualization of colocations 6 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion What is a colocation ? First, the data Spatial objects associated to different features • e.g. object 1 is characterized as "sparse vegetation" ( A ), object 7 as "mine" ( C ), and object 8 as "river erosion" ( B ) ➫ A 1 , C 7 and B 8 Then, the pattern Colocation = subset of features whose objects are "often" located close to each other • e.g. { A, C, B } , i.e. { sparse vegetation, mine, river erosion } Colocation instance = subset of objects having the features of the colocation and close to each other • set of all instances of a colocation = table instance TI Frédéric Flouvat A clustering-based visualization of colocations 7 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Mining colocations(Shekhar et al. 01) Two important aspects The neighborhood relationship • e.g. euclidean distance, intersection, ... The measure " often located close to each other" • participation index (anti-monotone) Mining Input : a set of spatial objects each one associated to a feature, a neighborhood relationship, and a threshold for the measure • data stored in a GIS Output : "frequent" colocations, i.e. those whose participation index is greater than a threshold Algorithm : classical levelwise mining algorithm • such as Apriori for itemset mining Frédéric Flouvat A clustering-based visualization of colocations 8 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Methods unsuited to expert needs Many works on colocations Improving algorithms performance Extracting local patterns Reducing the number of colocations ... Problems No visualization of colocations adapted to expert needs and practices • necessary to extract relevant informations Frédéric Flouvat A clustering-based visualization of colocations 9 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Visualizing data mining results Three main approaches to visualize data mining results : 1. Textual representation • basically a list of patterns with interestingness measures • ex. : textual visualization of colocation patterns ➫ simple but not easily understandable by domain experts Frédéric Flouvat A clustering-based visualization of colocations 10 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Visualizing data mining results Three main approaches to visualize data mining results : 2. Abstract representation (e.g. plots, matrices, graphs, trees or cubes) • condense and informative visual representations of the solutions with statistics • ex. : grid representation of association rules in MineSet (Brunk et al. 97) • ex. : radial hierarchical layout to represent frequent itemsets (Keim et al. 05) • ex. : orthogonal graphs to represent frequent itemsets (Leung et al. 08) ➫ not really adapted to spatial patterns • in spatial pattern mining, spatiality is not just an other dimension of analysis • for domain experts, the spatial dimension is the basis of their interpretation Frédéric Flouvat A clustering-based visualization of colocations 11 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Visualizing data mining results Three main approaches to visualize data mining results : 3. Cartographic representation • first solution : visualization of spatial pattern instances on a map • ex. : classical cartographic visualization of spatial clusters with colors • ex. : select an association rule and visualize its interestingness measure for each country (Andrienko 99) ➫ not possible to display all colocations instances (such as in spatial cluster analysis) ➫ "select a pattern and display its instances" gives only a local view of one pattern Frédéric Flouvat A clustering-based visualization of colocations 12 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Visualizing data mining results Three main approaches to visualize data mining results : 3. Cartographic representation • second solution : generating visual representations of the solutions • ex. : clusters of trajectories summarized by " representative trajectories " using a classifier and visual refinement (Andrienko 09) ➫ not directly usable for colocation patterns but an interesting approach Frédéric Flouvat A clustering-based visualization of colocations 13 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Outline 1 Context 2 Spatial pattern mining and visualization 3 Visualization of colocations 4 Application 5 Conclusion Frédéric Flouvat A clustering-based visualization of colocations 14 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion Our approach Problem How to visualize interesting colocations on a map ? Motivations Have a easily usable and interpretable visual representation for experts Give additional spatial and thematic informations Give a global cartographic view of the solutions Frédéric Flouvat A clustering-based visualization of colocations 15 / 36

Context Spatial pattern mining and visualization Visualization of colocations Application Conclusion A colored and labeled clique representation of colocations A natural visual representation of a colocation A clique node = object-type (i.e. feature) vertex = neighborhood relationship Example : Colocation { mining zone, sparse vegetation, sensitive trail, river erosion } Visual representation : Frédéric Flouvat A clustering-based visualization of colocations 16 / 36