C4.5: programs for machine learning
C4.5: programs for machine learning
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Machine Learning
Learning Classification Rules Using Lattices (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Integrating Classification and Association Rule Mining: A Concept Lattice Framework
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Building classification rules for case-based classifier using fuzzy sets and formal concept analysis
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
3DM: Domain-oriented Data-driven Data Mining
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
A concept lattice based outlier mining method in low-dimensional subspaces
Pattern Recognition Letters
Classification rule acquisition based on extended concept lattice
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Formal concept analysis in knowledge discovery: a survey
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Classification rule mining for a stream of perennial objects
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
3DM: Domain-oriented Data-driven Data Mining
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
Review: Formal Concept Analysis in knowledge processing: A survey on models and techniques
Expert Systems with Applications: An International Journal
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Concept lattice, core structure in Formal Concept Analysis has been used in various fields like software engineering and knowledge discovery.In this paper, we present the integration of Association rules and Classification rules using Concept Lattice. This gives more accurate classifiers for Classification. The algorithm used is incremental in nature. Any increase in the number of classes, attributes or transactions does not require the access to the previous database. The incremental behavior is very useful in finding classification rules for real time data such as image processing. The algorithm requires just one database pass through the entire database. Individual classes can have different support threshold and pruning conditions such as criteria for noise and number of conditions in the classifier.