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 )

Overview

Introduction

spatial data mining

Association Rule

Co-location Miner Algorithm

• 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

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

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

-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

Overview

Introduction

spatial data mining

Association Rule

Co-location Miner Algorithm

From Transactions to Neighborhoods

Transactions

Neighborhoods

-discrete, Independent, disjoint

-Continuous, Spatial related

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

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

• 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

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

THANK YOU