Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Self-Organization of Pulse-Coupled Oscillators with Application to Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Clustering Based on Modified Distance Measures
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
SyMP: an efficient clustering approach to identify clusters of arbitrary shapes in large data sets
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Unsupervised possibilistic clustering
Pattern Recognition
A curve tracing algorithm using level set based affine transform
Pattern Recognition Letters
Web Intelligence and Agent Systems
Pattern Recognition
A novel similarity measure for data clustering
Intelligent Data Analysis
MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & Knowledge Engineering
On the definition of functioning conditions of a mechanical system by means of orthogonal processing
WAMUS'05 Proceedings of the 5th WSEAS International Conference on Wavelet Analysis and Multirate Systems
Robust neural-fuzzy method for function approximation
Expert Systems with Applications: An International Journal
Possibilistic shell clustering of template-based shapes
IEEE Transactions on Fuzzy Systems
A time-domain-constrained fuzzy clustering method and its application to signal analysis
Fuzzy Sets and Systems
Clustering by competitive agglomeration
Pattern Recognition
An information-theoretic fuzzy C-spherical shells clustering algorithm
Fuzzy Sets and Systems
Comparison of conventional and rough K-means clustering
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Software project similarity measurement based on fuzzy C-means
ICSP'08 Proceedings of the Software process, 2008 international conference on Making globally distributed software development a success story
Relational generalizations of cluster validity indices
IEEE Transactions on Fuzzy Systems
Relational duals of cluster-validity functions for the c-means family
IEEE Transactions on Fuzzy Systems
On the definition of functioning conditions of a mechanical system by means orthogonal processing
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
ACMOS'05 Proceedings of the 7th WSEAS international conference on Automatic control, modeling and simulation
A genetic clustering algorithm using a message-based similarity measure
Expert Systems with Applications: An International Journal
Possibility theoretic clustering
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Possibilistic c-template clustering and its application in object detection in images
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
On the convergence of some possibilistic clustering algorithms
Fuzzy Optimization and Decision Making
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Traditionally, prototype-based fuzzy clustering algorithms such as the Fuzzy C Means (FCM) algorithm have been used to find “compact” or “filled” clusters. Recently, there have been attempts to generalize such algorithms to the case of hollow or “shell-like” clusters, i.e., clusters that lie in subspaces of feature space. The shell clustering approach provides a powerful means to solve the hitherto unsolved problem of simultaneously fitting multiple curves/surfaces to unsegmented, scattered and sparse data. In this paper, we present several fuzzy and possibilistic algorithms to detect linear and quadric shell clusters. We also introduce generalizations of these algorithms in which the prototypes represent sets of higher-order polynomial functions. The suggested algorithms provide a good trade-off between computational complexity and performance, since the objective function used in these algorithms is the sum of squared distances, and the clustering is sensitive to noise and outliers. We show that by using a possibilistic approach to clustering, one can make the proposed algorithms robust