1. DISCOVERING SPATIAL CO- LOCATION PATTERNS PRESENTED BY: REYHANEH JEDDI & SHICHAO YU (GROUP 21) CSCI 5707, PRINCIPLES OF DATABASE SYSTEMS, FALL 2013 CSCI 5707, PRINCIPLES OF DATABASE SYSTEMS, FALL 2013 11/26/2013 RELATION WITH THE COURSE IS CHAPTER 28 (DATA MINING )

2. Overview  Introduction  spatial data mining  Association Rule  Co-location Miner Algorithm

3. Data mining is finding some methods in large data sets and using stored data from data warehouse to analyze and manage the data to reduce future problems. Spatial Data mining is using the Data mining methods for spatial data and reaches some designs in data according to Geography  location, area and any same aspect.  Spatial data mining methods :  spatial OLAP and spatial data warehousing : Multi dimensional spatial databases  Characterization of spatial objects : Compare data distinctive  Spatial organization: Rules for city  Spatial allocation and indicator : Arrange countries  Spatial clustering : Bundling homes  Similarity analysis in spatial databases : Similar area

4.  Spatial databases  large scale and datasets  Spread domain : Ecology, Society safety, Health issues, ….  Map’s images  Various time : 20 to 100 Ecology  Co_accident  Spatial design  Co_location pattern Ecosystem data sets' spatial pattern : Local co_location pattern spatial co_location pattern Spatial data role Analyzing level  connection and narrowing Location role  space’s phenomena

5. ASSOCIATION RULE Example: l Association Rule--- analyzing and predicting –An implication expression of the form X  Y, where X and Y are itemsets –Example: {Milk, Diaper}  {Beer} l Rule Evaluation Metrics –Support (s)  Fraction of transactions that contain both X and Y –Confidence (c)  Measures how often items in Y appear in transactions that contain X l Given a set of transactions T, the goal of association rule mining is to find all rules having –support ≥ minsup threshold –confidence ≥ minconf threshold

6. -Order sensitive transactions -Support and confidence are ill-defined -May under-count support for a pattern -May over-counter support Limitations of Transactions on Spatial Data - Transaction over space - a priori algorithm

7. Overview  Introduction  spatial data mining  Association Rule  Co-location Miner Algorithm

8. From Transactions to Neighborhoods Transactions Neighborhoods -discrete, Independent, disjoint -Continuous, Spatial related

9. table instance 3/4 2/5 2/4 2/3 3/5 2/3 2/52/43/5 An Event centric co-location model

10. Illustration: Co-location Miner algorithm Generate candidate co-locations Participation indexes calculation Co-location rule generation

11. Event centric co-location model – Robust in face of overlapping neighborhoods Co-location Miner algorithm – Computational efficiency – High confidence low prevalence co-location patterns – Validity of inferences Advantage to Other Mining Methods

12. REFERENCES Book: Introduction to Data Mining, By Pang-Ning Tan; Michael Steinbach; Vipin Kumar 6 th Edition Articles : http://edugi.uji.es/Bacao/Geospatial%20Data%20Mining.pdf http://www.spatial.cs.umn.edu/paper_ps/sstd01.pdf http://en.wikipedia.org/wiki/Data_mining http://www.docstoc.com/docs/121010850/Spatial-Data-Mining---PowerPoint http://www.spatial.cs.umn.edu/paper_ps/co-location.pdf Pictures: http://www.spatial-accuracy.org/FromICCSA2008 http://gcn.com/articles/2008/11/14/the-state-of-spatial-data.aspx http://www.ec-gis.org/Workshops/7ec-gis/papers/html/gitis/gitis.htm http://www.spatialdatamining.org/software http://www.spatialdatamining.org/ http://www.geocomputation.org/2000/GC059/Gc059.htm

13. THANK YOU