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International Journal of Man-Machine Studies
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning internal representations by error propagation
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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
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
Machine Learning
Machine Learning
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
A comparison of fuzzy strategies for corporate acquisition analysis
Fuzzy Sets and Systems
On fuzzy cluster validity indices
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A hybrid particle swarm optimization approach for clustering and classification of datasets
Knowledge-Based Systems
Information Technology and Management
WSEAS Transactions on Information Science and Applications
| Hi-index | 12.07 |
This study proposes a method of cluster validity index that simultaneously provide the measurements of goodness of clustering on clustered data and of classification accuracy for complicated information systems based upon the PBMF-index method and rough set (RS) theory. The maximum value of this index, called the Huang-index, not only provides the best partitioning, but also obtains the optimal accuracy of classification for the approximation sets. The traditional PBMF-index method is only used to ensure the formation of a small number of compact clusters with large separation between at least two clusters. In contrast to the traditional PBMF-index method, the Huang-index method extends the applications of unsupervised optimal cluster to the fields of classification. In the proposed algorithm, all the attributes of the data are first clustered into groups using the Fuzzy C-means (FCM) method. The clustered data are then used to identify approximate regions and classification accuracy and to calculate centroids of clusters for decision attribute based on the RS theory. Finally, all those calculated data are put into the proposed index method to find the cluster validity index. The validity of the proposed approach is demonstrated using the data derived from a hypothetical function of two independent variables and electronic stock data extracted from the financial database maintained by the Taiwan Economic Journal (TEJ). The clustering results obtained using the proposed method are compared with the results obtained using the traditional PBMF-index partition method. The effects of the number of clusters on the partitions of clusters and the RS regions are systematically examined and compared. The results show that the proposed Huang-index method not only yields a superior clustering capability than the traditional clustering algorithm, but also yields a reliable classification and obtains a set of suitable decision rules extracted from the RS theory.