Algorithms for clustering data
Algorithms for clustering data
Multilayer feedforward networks are universal approximators
Neural Networks
Instance-Based Learning Algorithms
Machine Learning
Feature extraction through LOCOCODE
Neural Computation
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Naive Bayesian Classification of Structured Data
Machine Learning
Test-Cost Sensitive Naive Bayes Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A comparison of event models for Naive Bayes anti-spam e-mail filtering
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Kernel independent component analysis for gene expression data clustering
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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Particle classification is one of the major analyses in high-energy particle physics experiments. We design a classification framework combining classification and clustering for particle physics experiments data. The system involves classification by a set of Artificial Neural Networks (ANN); each using distinct subsets of samples selected from the general set. We use frequent variable sets based clustering for partitioning the train samples into several natural subsets, then standard back-propagation ANNs are trained on them. The final decision for each test case is a two-step process. First, the nearest cluster is found for the case, and then the decision is based on the ANN classifier trained on the specific cluster. Comparisons with other classification and clustering methods show that our method is promising.