582600 Spatial Data Mining (4 op, 2 ov)
Spring 2007
- Antti Leino
- Lectures 12.3.-26.4.2007, Mon, Thu 10-12 C222
- Exam Thu, 3.5., 16-19
- Project work by Wed, 16.5.
- Course page (this page): http://www.cs.helsinki.fi/u/leino/opetus/spatial-k07/
Overview
The course covers exploratory methods for analysing data with a spatial component, with a slight emphasis on point data. Main topics in the course include modelling spatial dependency, discovering association rules, and spatial clustering.
Roughly half of the time specified as "lectures" will be lectures, the rest is a seminar-like discussion of research papers. In addition to these, there will be a project work.
Status
This is a graduate-level course in Computer Science. It is primarily intended as a part of the Master's Degree Programme in Geoinformatics, but students outside of the programme are also welcome.
Prerequisites
Some familiarity with computer science, spatial data and statistics are assumed. If necessary, take a look at the contents of the following courses:
- Data Structures
- Spatial Data Processing
- Introduction to Probability Theory
- Introduction to Statistics
Schedule
- 12.3.
- 15.3.
- 19.3.
-
- Huang et al. 2004: Discovering
Colocation Patterns from Spatial Data Sets: A General
Approach. IEEE Transactions on Knowledge and Data
Engineering 16(12)
Joona Lehtomäki - Salmenkivi 2006.: Efficient Mining of
Correlation Patterns in Spatial Point Data. Proceedings of
10th European Conference on Principles and Practice of Knowledge
Discovery in Databases (PKDD-06).
Daniela Hellgren
- Huang et al. 2004: Discovering
Colocation Patterns from Spatial Data Sets: A General
Approach. IEEE Transactions on Knowledge and Data
Engineering 16(12)
- 22.3.
-
- Yoo et al. 2006: A Joinless Approach for Mining
Spatial Colocation Patterns. IEEE Transactions on
Knowledge and Data Engineering 18(10).
Davin Wong - slides - Huang et al. 2005: Can We Apply Projection Based Frequent
Pattern Mining Paradigm to Spatial Colocation Mining?
Advances in Knowledge Discovery and Data Mining. Proceedings of the
9th Pacific-Asia Conference, PAKDD 2005
Zoltán Bójás
- Yoo et al. 2006: A Joinless Approach for Mining
Spatial Colocation Patterns. IEEE Transactions on
Knowledge and Data Engineering 18(10).
- 26.3.
-
- Xiong et al. 2004: A Framework for Discovering
Co-location Patterns in Data Sets with Extended Spatial
Objects. Proceedings of SIAM International Conference on
Data Mining (SDM)
Paula Silvonen - Yoo et al. 2006: Discovery of Co-evolving Spatial
Event Sets. Proceedings of the SIAM International
Conference on Data Mining (SDM)
Timo Nurmi
- Xiong et al. 2004: A Framework for Discovering
Co-location Patterns in Data Sets with Extended Spatial
Objects. Proceedings of SIAM International Conference on
Data Mining (SDM)
- 29.3.
-
- Estivill-Castro & Lee 2001: Data Mining Techniques
for Autonomous Exploration of Large Volumes of Geo-referenced
Crime Data
Rafal Zarajczyk - slides - Spatial clustering: an introduction
- Estivill-Castro & Lee 2001: Data Mining Techniques
for Autonomous Exploration of Large Volumes of Geo-referenced
Crime Data
- 2.4.
-
- Tung et al. 2001: Spatial Clustering in the Presence of
Obstacles. Proceedings of the 17th International
Conference on Data Engineering
Milan Magdics - slides - Wang & Hamilton 2003: DBRS: A Density-Based Spatial
Clustering Method with Random Sampling. Proceedings of the
7th Pacific-Asia Conference on Advances in Knowledge Discovery
and Data Mining: PAKDD 2003
Bence Novák - slides
- Tung et al. 2001: Spatial Clustering in the Presence of
Obstacles. Proceedings of the 17th International
Conference on Data Engineering
- 16.4.
- 19.4.
-
- Kazar et al. 2004: Comparing Exact and Approximate Spatial
Auto-Regression Model Solutions for Spatial Data Analysis.
Proceedings of the Third International Conference on
Geographic Information Science (GIScience2004)
Magnus Udd - slides
- Kazar et al. 2004: Comparing Exact and Approximate Spatial
Auto-Regression Model Solutions for Spatial Data Analysis.
Proceedings of the Third International Conference on
Geographic Information Science (GIScience2004)
- 23.4.
-
- Kavouras 2001: Understanding and Modelling Spatial
Change Raper -- Cheylan (Eds.): Life and Motion of
Socio-Economic Units, Chapter 4. GISDATA Series 8
Sandeep Puthan Purayil - slides - Shekhar et al. 2003: A Unified Approach to Detecting
Spatial Outliers. GeoInformatica 7(2)
Pekka Maksimainen - slides
- Kavouras 2001: Understanding and Modelling Spatial
Change Raper -- Cheylan (Eds.): Life and Motion of
Socio-Economic Units, Chapter 4. GISDATA Series 8
- 26.4.
Project work
- E-mail as a pdf file by 16.5.
- Option 1: course diary
- About half a page per session
- What did you learn this time?
- What was good? What was bad?
- Option 2: essay
- About five pages
- Based on two articles not covered during the course
- Choose from one of the following sites:
- Option 3: data mining exercise
- Report on a mining experiment
- Option 3.1: implementation of an algorithm
- Option 3.2: mining and analysis of own spatial data
Antti Leino