1 / 15

Spatial Data Mining

Spatial Data Mining. Ashkan Zarnani Sadra Abedinzadeh Farzad Peyravi. From DM to KDD. DM is a step in KDD Extracting useful, meaningful patterns Five terabyte of data collected each day in NASA This is used to discover stars, galaxies etc. Spatial Data.

ami
Télécharger la présentation

Spatial Data Mining

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Spatial Data Mining Ashkan Zarnani Sadra Abedinzadeh Farzad Peyravi

  2. From DM to KDD • DM is a step in KDD • Extracting useful, meaningful patterns • Five terabyte of data collected each day in NASA • This is used to discover stars, galaxies etc.

  3. Spatial Data • Any kind of data that has one or more fields concerning with location, shape , area and similar attributes • Point, Line, Polygon • Spatial Access Methods (SAMs) • Information in a GIS is organized in “layers”. • For example a map will have a layer of “roads”, “train stations”, “suburbs” and “water bodies

  4. Layers in GIS • People • Commercial • Governmental • Geographical • Traffic • Business

  5. Spatial Queries & SAM

  6. Spatial Data Mining Methods • Spatial OLAP and spatial data warehousing • Drilling, dicing and pivoting on multi-dimensional spatial databases • Generalization & characterization of spatial objects • Summarize & contrast data characteristics, e.g., dry vs. wet regions • Spatial Association: • Find rules like “inside(x, city) à near(x, highway)”. • Spatial classification and prediction • Classify countries based on climate • Spatial clustering and outlier analysis • Cluster houses to find distribution patterns • Similarity analysis in spatial databases • Find similar regions in a large set of maps

  7. SDM : State of the Art Progressive Refinement Finding Coarse Relationships and then extracting the non-candidate rules to avoid complex spatial operations for all objects g_close_to  candidates  detail process

  8. SDM : State of the Art Multilevel Rules Finding rules in several levels of the concept hierarchies ContinentCountryProvinceCityZoneBlock Water( flow(river, channel) – nonflow(sea, lake, ocean) )

  9. SDM : State of the Art Quantitative Rules The challenge of treating continuous attributes, the sharp boundaries Fuzziness applied for realistic knowledge extraction

  10. SDM : State of the Art OLAM OnLine Analytical Mining, the user can interact with the mining progress: Data sets, Concept Hierarchies, Interestingness Measures, Type of Knowledge, Representation GMQL is proposed and is being extended

  11. References • [1] Floris Geerts, Sofie Haesevoets and Bart Kuijpers. • A Theory of Spatio-Temporal Database. Computer Science Dept., North Dakota State University (2000) • [2] Martin Ester, Hans-Peter Kriegel, Jörg Sander.Algorithms and Applications for Spatial Data Mining , Geographic Data Mining and Knowledge Discovery, 2001. • [3] Martin Ester, Alexander Frommelt, Hans-Peter Kriegel, Jörg Sander. Algorithms for Characterization and Trend Detection in Spatial Databases, International Conference on Knowledge Discovery and Data Mining (KDD-98) • [4] Jan Paredaens, Bart Kuijpers. Data Models and Query Languages for Spatial Databases. ACMSIGKDD Explorations (1999) • [5] Hans-Peter Kriegel, Thomas Brinkhoff, Ralf Schneider. Efficient Spatial Query Processing in Geographic Database Systems. VLDB (2001) • [6] Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. From Data Mining to Knowledge Discovery in Databases. AI MAGAZINE (1999) • [7] Ramakrishnan Srikant, Rakesh Agrawal. Mining Quantitative Association Rules in Large Relational Tables. VLDB (1996) • [8] Krzysztof Koperski,  A Progressive Refinement  Approach to Spatial Data Mining. SFU PhD Thesis (1999)

  12. Thanks For Your Attention!

More Related