Fuzzy clustering with a knowledge-based guidance
Pattern Recognition Letters
Influential Rule Search Scheme (IRSS)-A New Fuzzy Pattern Classifier
IEEE Transactions on Knowledge and Data Engineering
General C-Means Clustering Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-model modelling and predictive control based on local model networks
Control and Intelligent Systems
Unsupervised possibilistic clustering
Pattern Recognition
Output value-based initialization for radial basis function neural networks
Neural Processing Letters
Personalized information retrieval system in the framework of fuzzy logic
Expert Systems with Applications: An International Journal
Robust fuzzy clustering-based image segmentation
Applied Soft Computing
PFHC: A clustering algorithm based on data partitioning for unevenly distributed datasets
Fuzzy Sets and Systems
Clustering: A neural network approach
Neural Networks
Auto-adaptive neural network tree structure based on complexity estimator
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Robust interval type-2 possibilistic C-means clustering and its application for fuzzy modeling
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 4
A gradient-descent-based approach for transparent linguistic interface generation in fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Novel segmentation algorithm in segmenting medical images
Journal of Systems and Software
Robust kernel FCM in segmentation of breast medical images
Expert Systems with Applications: An International Journal
Possibilistic approach to biclustering: an application to oligonucleotide microarray data analysis
CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
Approximating i/o data using radial basis functions: a new clustering-based approach
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Analyzing software effort estimation using k means clustered regression approach
ACM SIGSOFT Software Engineering Notes
Feature weighted unsupervised classification algorithm and adaptation for software cost estimation
International Journal of Computational Intelligence Studies
| Hi-index | 0.00 |
Many clustering models define good clusters as extrema of objective functions. Optimization of these models is often done using an alternating optimization (AO) algorithm driven by necessary conditions for local extrema. We abandon the objective function model in favor of a generalized model called alternating cluster estimation (ACE). ACE uses an alternating iteration architecture, but membership and prototype functions are selected directly by the user. Virtually every clustering model can be realized as an instance of ACE. Out of a large variety of possible instances of non-AO models, we present two examples: 1) an algorithm with a dynamically changing prototype function that extracts representative data and 2) a computationally efficient algorithm with hyperconic membership functions that allows easy extraction of membership functions. We illustrate these non-AO instances on three problems: a) simple clustering of plane data where we show that creating an unmatched ACE algorithm overcomes some problems of fuzzy c-means (FCM-AO) and possibilistic c-means (PCM-AO); b) functional approximation by clustering on a simple artificial data set; and c) functional approximation on a 12 input 1 output real world data set. ACE models work pretty well in all three cases