discovering spatial co- location patterns presented by: reyhaneh jeddi & shichao yu (group 21)...
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DISCOVERING SPATIAL CO-LOCATION PATTERNS
PRESENTED BY: REYHANEH JEDDI & SHICHAO YU (GROUP 21)
CSCI 5707, PRINCIPLES OF DATABASE SYSTEMS, FALL 2013
11/26/2013
RELATION WITH THE COURSE IS CHAPTER 28 (DATA MINING )
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Overview
Introduction
spatial data mining
Association Rule
Co-location Miner Algorithm
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• 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
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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
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ASSOCIATION RULE
Example:
Beer}{}Diaper,Milk{
4.052
|T|)BeerDiaper,,Milk(
s
67.032
)Diaper,Milk()BeerDiaper,Milk,(
c
Association Rule--- analyzing and predicting– An implication expression of the form X Y, where X
and Y are itemsets
– Example: {Milk, Diaper} {Beer}
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 thatcontain X
Given a set of transactions T, the goal of association rule mining is to find all rules having
– support ≥ minsup threshold
– confidence ≥ minconf threshold
TID Items
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3 Milk, Diaper, Beer, Coke
4 Bread, Milk, Diaper, Beer
5 Bread, Milk, Diaper, Coke
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-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
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Overview
Introduction
spatial data mining
Association Rule
Co-location Miner Algorithm
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From Transactions to Neighborhoods
Transactions
Neighborhoods
-discrete, Independent, disjoint
-Continuous, Spatial related
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table instance
3/4 2/5 2/4 2/3 3/5 2/3
2/5 2/4 3/5
An Event centric co-location model
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Illustration: Co-location Miner algorithm Generate candidate co-locations Participation indexes calculation Co-location rule generation
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• 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
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REFERENCESBook:• Introduction to Data Mining, By Pang-Ning Tan; Michael Steinbach; Vipin Kumar 6th 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
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THANK YOU