C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Robust Classification for Imprecise Environments
Machine Learning
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Using conjunction of attribute values for classification
Proceedings of the eleventh international conference on Information and knowledge management
Data Mining and Knowledge Discovery
IEEE Intelligent Systems
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Comparisons of Classification Methods for Screening Potential Compounds
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
The predictive toxicology evaluation challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Reasoning about Molecular Similarity and Properties
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Summarizing itemset patterns: a profile-based approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Weighted decomposition kernels
ICML '05 Proceedings of the 22nd international conference on Machine learning
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
Generating semantic annotations for frequent patterns with context analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Load Balancing for the Distributed Mining of Molecular Structures
IEEE Transactions on Parallel and Distributed Systems
XML structural delta mining: issues and challenges
Data & Knowledge Engineering - Special issue: ER 2003
Decentralized load balancing for highly irregular search problems
Microprocessors & Microsystems
Learning from interpretations: a rooted kernel for ordered hypergraphs
Proceedings of the 24th international conference on Machine learning
Mining complex power networks for blackout prevention
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering frequent geometric subgraphs
Information Systems
Semantic annotation of frequent patterns
ACM Transactions on Knowledge Discovery from Data (TKDD)
Classifying Chemical Compounds Using Contrast and Common Patterns
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Classification of Web Documents Using a Graph-Based Model and Structural Patterns
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
GADDI: distance index based subgraph matching in biological networks
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A Method for Classifying Vertices of Labeled Graphs Applied to Knowledge Discovery from Molecules
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Capacity Control for Partially Ordered Feature Sets
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
gPrune: a constraint pushing framework for graph pattern mining
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Text classification using graph mining-based feature extraction
Knowledge-Based Systems
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
An approach for adaptive associative classification
Expert Systems with Applications: An International Journal
High performance subgraph mining in molecular compounds
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
Cyclic pattern kernels revisited
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A simple, structure-sensitive approach for web document classification
AWIC'05 Proceedings of the Third international conference on Advances in Web Intelligence
Learning from graph data by putting graphs on the lattice
Expert Systems with Applications: An International Journal
An evolutionary approach to rank class association rules with feedback mechanism
Expert Systems with Applications: An International Journal
An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Mining closed patterns in relational, graph and network data
Annals of Mathematics and Artificial Intelligence
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In this paper we study the problem of classifying chemical compounddatasets. We present a sub-structure-based classificationalgorithm that decouples the sub-structure discovery processfrom the classification model construction and uses frequentsubgraph discovery algorithms to find all topological and geometricsub-structures present in the dataset. The advantage ofour approach is that during classification model construction, allrelevant sub-structures are available allowing the classifier tointelligently select the most discriminating ones. The computationalscalability is ensured by the use of highly efficient frequentsubgraph discovery algorithms coupled with aggressive featureselection. Our experimental evaluation on eight different classificationproblems shows that our approach is computationallyscalable and on the average, outperforms existing schemes by10% to 35%.