On the meaning of Dunn's partition coefficient for fuzzy clusters
Fuzzy Sets and Systems
Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Machine Learning - Special issue on learning with probabilistic representations
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Machine Learning
Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Applying rough sets to market timing decisions
Decision Support Systems - Special issue: Data mining for financial decision making
A new approach for measuring the validity of the fuzzy c-means algorithm
Advances in Engineering Software
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Computers and Operations Research
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
An improved accuracy measure for rough sets
Journal of Computer and System Sciences
A comparison of fuzzy strategies for corporate acquisition analysis
Fuzzy Sets and Systems
On fuzzy cluster validity indices
Fuzzy Sets and Systems
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
Application of elitist multi-objective genetic algorithm for classification rule generation
Applied Soft Computing
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
A new measure of uncertainty based on knowledge granulation for rough sets
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A genetic algorithm with gene rearrangement for K-means clustering
Pattern Recognition
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A fuzzy c-means based hybrid evolutionary approach to the clustering of supply chain
Computers and Industrial Engineering
Hi-index | 0.00 |
This study proposes a method, designated as the GRP-index method, for the classification of continuous value datasets in which the instances do not provide any class information and may be imprecise and uncertain. The proposed method discretizes the values of the individual attributes within the dataset and achieves both the optimal number of clusters and the optimal classification accuracy. The proposed method consists of a genetic algorithm (GA) and an FRP-index method. In the FRP-index method, the conditional and decision attribute values of the instances in the dataset are fuzzified and discretized using the Fuzzy C-means (FCM) method in accordance with the cluster vectors given by the GA specifying the number of clusters per attribute. Rough set (RS) theory is then applied to determine the lower and upper approximate sets associated with each cluster of the decision attribute. The accuracy of approximation of each cluster of the decision attribute is then computed as the cardinality ratio of the lower approximate sets to the upper approximate sets. Finally, the centroids of the lower approximate sets associated with each cluster of the decision attribute are determined by computing the mean conditional and decision attribute values of all the instances within the corresponding sets. The cluster centroids and accuracy of approximation are then processed by a modified form of the PBMF-index function, designated as the RP-index function, in order to determine the optimality of the discretization/classification results. In the event that the termination criteria are not satisfied, the GA modifies the initial population of cluster vectors and the FCM, RS and RP-index function procedures are repeated. The entire process is repeated iteratively until the termination criteria are satisfied. The maximum value of the RP cluster validity index is then identified, and the corresponding cluster vector is taken as the optimal classification result. The validity of the proposed approach is confirmed by cross validation, and by comparing the classification results obtained for a typical stock market dataset with those obtained by non-supervised and pseudo-supervised classification methods. The results show that the proposed GRP-index method not only has a better discretization performance than the considered methods, but also achieves a better accuracy of approximation, and therefore provides a more reliable basis for the extraction of decision-making rules.