A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fuzzy control model (FCM) for dynamic portfolio management
Fuzzy Sets and Systems
MR functional cardiac imaging: segmentation, measurement and WWW based visualisation of 4D data
Future Generation Computer Systems - Special issue on ITIS—an international telemedical information society
Cluster Validation with Generalized Dunn's Indices
ANNES '95 Proceedings of the 2nd New Zealand Two-Stream International Conference on Artificial Neural Networks and Expert Systems
A recommender system using GA K-means clustering in an online shopping market
Expert Systems with Applications: An International Journal
A currency crisis and its perception with fuzzy C-means
Information Sciences: an International Journal
An intelligent market segmentation system using k-means and particle swarm optimization
Expert Systems with Applications: An International Journal
Fuzzy c-means clustering with prior biological knowledge
Journal of Biomedical Informatics
Using the self organizing map for clustering of text documents
Expert Systems with Applications: An International Journal
Using self-organising maps in the detection and recognition of road signs
Image and Vision Computing
Expert Systems with Applications: An International Journal
Application of ant K-means on clustering analysis
Computers & Mathematics with Applications
Using genetic algorithm to support portfolio optimization for index fund management
Expert Systems with Applications: An International Journal
Segmentation of stock trading customers according to potential value
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Validity-guided (re)clustering with applications to image segmentation
IEEE Transactions on Fuzzy Systems
Stock market co-movement assessment using a three-phase clustering method
Expert Systems with Applications: An International Journal
A medical procedure-based patient grouping method for an emergency department
Applied Soft Computing
Hi-index | 12.05 |
In this paper a data mining approach for classification of stocks into clusters is presented. After classification, the stocks could be selected from these groups for building a portfolio. It meets the criterion of minimizing the risk by diversification of a portfolio. The clustering approach categorizes stocks on certain investment criteria. We have used stock returns at different times along with their valuation ratios from the stocks of Bombay Stock Exchange for the fiscal year 2007-2008. Results of our analysis show that K-means cluster analysis builds the most compact clusters as compared to SOM and Fuzzy C-means for stock classification data. We then select stocks from the clusters to build a portfolio, minimizing portfolio risk and compare the returns with that of the benchmark index, i.e. Sensex.