Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-resolution landform classification using fuzzy k-means
Fuzzy Sets and Systems - Special issue on Uncertainty in geographic information systems and spatial data
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Assessing clustering reliability and features informativeness by random permutations
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Hi-index | 0.00 |
Experts classifications of spatial data are strongly affected by subjectivity and rigidity of rules. They do not take into account, in a quantitative way, the overlap of classes and as a major consequence, their classifications are often not reproducibles. To overcome this subjectivity, exploratory techniques can suggest a coherent set of rules that will produce suitable polythetic and overlapping classes. The aim of this paper is to validate the unsupervised method of fuzzy clustering applied to classification of raster spatial data.