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
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Hints for Adaptive Problem Solving Gleaned from Immune Networks
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm
Genetic Programming and Evolvable Machines
Revisiting Negative Selection Algorithms
Evolutionary Computation
Research on Vehicle Image Classifier Based on Concentration Regulating of Immune Clonal Selection
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 06
Associative classification with artificial immune system
IEEE Transactions on Evolutionary Computation
Quiet in class: classification, noise and the dendritic cell algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Clonal selection algorithms: a comparative case study using effective mutation potentials
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An artificial immune system approach to associative classification
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part I
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Data mining is the process of discovering patterns from large data sets. One of the branches of data mining is Associative Classification (AC). AC mining is a promising approach that uses association rules discovery techniques to construct association classifiers. However, traditional AC algorithms typically search for all possible association rules to find a representative subset of those rules. Since the search space of such rules may grow exponentially as the support threshold decreases, the rules discovery process can be computationally expensive. One effective way to tackle this problem is to directly find a set of high-stakes association rules that potentially builds a highly accurate classifier. This paper introduces AC-CS, a novel AC algorithm, inspired by the clonal selection of the immune system. The algorithm proceeds in an evolutionary fashion to populate only rules that are likely to yield good classification accuracy. Empirical results on several real datasets show that the approach generates dramatically less rules than traditional AC algorithms. Hence, the proposed approach is indeed significantly more efficient than traditional AC algorithms while achieving a competitive accuracy.