Data mining: concepts and techniques
Data mining: concepts and techniques
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
A Modified Chi2 Algorithm for Discretization
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
NeuroRule: A Connectionist Approach to Data Mining
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
IEEE Transactions on Knowledge and Data Engineering
CLIP4: hybrid inductive machine learning algorithm that generates inequality rules
Information Sciences: an International Journal - Special issue: Soft computing data mining
A greedy algorithm for supervised discretization
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
An Extended Chi2 Algorithm for Discretization of Real Value Attributes
IEEE Transactions on Knowledge and Data Engineering
A Discretization Algorithm Based on a Heterogeneity Criterion
IEEE Transactions on Knowledge and Data Engineering
Discovering Trends in Large Datasets Using Neural Networks
Applied Intelligence
Decision-tree instance-space decomposition with grouped gain-ratio
Information Sciences: an International Journal
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
Neural-Based Learning Classifier Systems
IEEE Transactions on Knowledge and Data Engineering
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Data discretization unification
Knowledge and Information Systems
An extension of the naive Bayesian classifier
Information Sciences: an International Journal
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Improvement of decision accuracy using discretization of continuous attributes
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
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In this paper we propose a new static, global, supervised, incremental and bottom-up discretization algorithm based on coefficient of dispersion and skewness of data range. It automates the discretization process by introducing the number of intervals and stopping criterion. The results obtained using this discretization algorithm show that the discretization scheme generated by the algorithm almost has minimum number of intervals and requires smallest discretization time. The feedforward neural network with conjugate gradient training algorithm is used to compute the accuracy of classification from the data discretized by this algorithm. The efficiency of the proposed algorithm is shown in terms of better discretization scheme and better accuracy of classification by implementing it on six different real data sets.