Neural networks and the bias/variance dilemma
Neural Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Mining frequent neighboring class sets in spatial databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Relational Data Mining
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Discovery of relational association rules
Relational Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Reasoning about Binary Topological Relations
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Spatial Data Mining: A Database Approach
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Integrated spatial reasoning in geographic information systems: combining topology and direction
Integrated spatial reasoning in geographic information systems: combining topology and direction
A progressive refinement approach to spatial data mining
A progressive refinement approach to spatial data mining
Inducing Multi-Level Association Rules from Multiple Relations
Machine Learning
Redundant feature elimination for multi-class problems
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Naive Bayesian Classification of Structured Data
Machine Learning
Discovery of spatial association rules in geo-referenced census data: A relational mining approach
Intelligent Data Analysis
Spatial reasoning based spatial data mining for precision agriculture
APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
Spatial contextual classification and prediction models for mining geospatial data
IEEE Transactions on Multimedia
RELATIONAL DATA MINING AND ILP FOR DOCUMENT IMAGE UNDERSTANDING
Applied Artificial Intelligence
Transductive Learning from Relational Data
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Discovering Relational Emerging Patterns
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Emerging Pattern Based Classification in Relational Data Mining
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
A relational approach to probabilistic classification in a transductive setting
Engineering Applications of Artificial Intelligence
Global and local spatial autocorrelation in predictive clustering trees
DS'11 Proceedings of the 14th international conference on Discovery science
Outlier detection in relational data: A case study in geographical information systems
Expert Systems with Applications: An International Journal
Classification based on specific rules and inexact coverage
Expert Systems with Applications: An International Journal
Reducing the size of databases for multirelational classification: a subgraph-based approach
Journal of Intelligent Information Systems
Genetic algorithm-based optimized association rule mining for multi-relational data
Intelligent Data Analysis
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In Spatial Data Mining, spatial dimension adds a substantial complexity to the data mining task. First, spatial objects are characterized by a geometrical representation and relative positioning with respect to a reference system, which implicitly define both spatial relationships and properties. Second, spatial phenomena are characterized by autocorrelation, i.e., observations of spatially distributed random variables are not location-independent. Third, spatial objects can be considered at different levels of abstraction (or granularity). The recently proposed SPADA algorithm deals with all these sources of complexity, but it offers a solution for the task of spatial association rules discovery. In this paper the problem of mining spatial classifiers is faced by building an associative classification framework on SPADA. We consider two alternative solutions for associative classification: a propositional and a structural method. In the former, SPADA obtains a propositional representation of training data even in spatial domains which are inherently non-propositional, thus allowing the application of traditional data mining algorithms. In the latter, the Bayesian framework is extended following a multi-relational data mining approach in order to cope with spatial classification tasks. Both methods are evaluated and compared on two real-world spatial datasets and results provide several empirical insights on them.