Introduction to Grey system theory
The Journal of Grey System
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Software Engineering Economics
Software Engineering Economics
Using Grey Relational Analysis to Predict Software Effort with Small Data Sets
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
METRICS '05 Proceedings of the 11th IEEE International Software Metrics Symposium
Best of both: a hybridized centroid-medoid clustering heuristic
Proceedings of the 24th international conference on Machine learning
A General Empirical Solution to the Macro Software Sizing and Estimating Problem
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Fuzzy grey relational analysis for software effort estimation
Empirical Software Engineering
Research on Software Effort Estimation Combined with Genetic Algorithm and Support Vector Regression
ISCCS '11 Proceedings of the 2011 International Symposium on Computer Science and Society
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Alternating cluster estimation: a new tool for clustering and function approximation
IEEE Transactions on Fuzzy Systems
Robust TSK fuzzy modeling for function approximation with outliers
IEEE Transactions on Fuzzy Systems
Switching regression models and fuzzy clustering
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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Multi-attribute data, needed to be clustered may have different consequences of attributes on clustering criteria. In this paper, a new soft clustering technique is proposed in which similarity measures between data points and impact of each attribute is calculated using grey relational analysis. Algorithm provides the flexibility to choose significant number of attributes for classification purpose using feature subset selection. An iterative approach is adopted to find desired number of clusters having more appropriate and unique centroid. In addition, the use of proposed technique is instanced on software cost estimation because inherent uncertainty in software attributes due to the measurement by expert judgment has a significant impact on estimation accuracy. Combination of clustering and regression technique reduces the potential problem in efficacy of predictive assays due to heterogeneity of the data. Clustered approach creates the subsets of data having a degree of homogeneity that elaborate more accurate prediction.