On the meaning of Dunn's partition coefficient for fuzzy clusters
Fuzzy Sets and Systems
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
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning - Special issue on learning with probabilistic representations
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Computers and Operations Research
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
A fusion model of HMM, ANN and GA for stock market forecasting
Expert Systems with Applications: An International Journal
A novel approach to fuzzy rough sets based on a fuzzy covering
Information Sciences: an International Journal
A comparison of fuzzy strategies for corporate acquisition analysis
Fuzzy Sets and Systems
On fuzzy cluster validity indices
Fuzzy Sets and Systems
A novel conflict reassignment method based on grey relational analysis (GRA)
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
On the topological properties of fuzzy rough sets
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
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This paper presents an automatic stock portfolio selection system. In the proposed approach, 53 financial indices are collected for each stock item and are consolidated into six financial ratios [Grey relational grades (GRGs)] using a Grey relational analysis model. The GRGs are processed using a modified form of the PBMF index method (designated as the Huang index function) to determine the optimal number of clusters per GRG. The resulting cluster indices are then processed using rough set theory to identify the stocks within the lower approximate sets. Finally, the GRGs of each stock item in the lower approximate sets are consolidated into a single GRG, indicating the ability of the stock item to maximize the rate of return. It is demonstrated that the proposed stock selection mechanism yields a higher rate of return than several existing portfolio selection systems.