C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Machine Learning - Special issue on learning with probabilistic representations
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and 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
Improving an Association Rule Based Classifier
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Information-Based Classification by Aggregating Emerging Patterns
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
A bottom-up projection based algorithm for mining high utility itemsets
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
ACIK: association classifier based on itemset kernel
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Effective utility mining with the measure of average utility
Expert Systems with Applications: An International Journal
Integration of multiple fuzzy FP-trees
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Incrementally mining high utility patterns based on pre-large concept
Applied Intelligence
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The classification problem in data mining is to discover models from training data for classifying unknown instances Associative classification builds the classifier rules using association rules and it is more accurate compared to previous methods In this paper, a new method named CSFP that builds a classifier from strong frequent patterns without the need to generate association rules is presented We address the rare item problem by using a partitioning method Rules generated are stored using a compact data structure named CP-Tree and a series of pruning methods are employed to discard weak frequent patterns Experimental results show that our classifier is more accurate than previous associative classification methods as well as other state-of-the-art non-associative classifiers.