Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Knowledge-Based Visualization to Support Spatial Data Mining
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
A Joinless Approach for Mining Spatial Colocation Patterns
IEEE Transactions on Knowledge and Data Engineering
Mining Maximal Generalized Frequent Geographic Patterns with Knowledge Constraints
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
Intelligent Data Analysis
Discovering Emerging Patterns in Spatial Databases: A Multi-relational Approach
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Zonal Co-location Pattern Discovery with Dynamic Parameters
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
WiFIsViz: Effective Visualization of Frequent Itemsets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A Visual Analytics Toolkit for Cluster-Based Classification of Mobility Data
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
The iZi project: easy prototyping of interesting pattern mining algorithms
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
A constrained frequent pattern mining system for handling aggregate constraints
Proceedings of the 16th International Database Engineering & Applications Sysmposium
Mining probabilistic datasets vertically
Proceedings of the 16th International Database Engineering & Applications Sysmposium
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Extraction of interesting colocations in geo-referenced data is one of the major tasks in spatial pattern mining. The goal is to find sets of spatial object-types with instances located in the same neighborhood. In this context, the main drawback is the visualization and interpretation of extracted patterns by domain experts. Indeed, common textual representation of colocations loses important spatial information such as the position, the orientation or the spatial distribution of the patterns. To overcome this problem, we propose a new clustering-based visualization technique deeply integrated in the colocation mining algorithm. This new simple, concise and intuitive cartographic visualization considers both spatial information and expert practices. This proposition has been integrated in a Geographic Information System and experimented on a real-world geological data set. Domain experts confirm the added-value of this visualization approach.