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

• 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

Layers in GIS

PeopleCommercialGovernmentalGeographicalTrafficBusiness

Spatial Queries & SAM

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) 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

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

SDM : State of the Art

Multilevel Rules

Finding rules in several levels of the concept hierarchies

ContinentCountryProvinceCityZoneBlock

Water( flow(river, channel) – nonflow(sea, lake, ocean) )

SDM : State of the Art

Quantitative Rules

The challenge of treating continuous attributes, the sharp boundaries

Fuzziness applied for realistic knowledge extraction

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

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. ACM

SIGKDD 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)

Thanks For Your Attention!