New geometric inference techniques for type-2 fuzzy sets
International Journal of Approximate Reasoning
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Interval type-2 fuzzy membership function generation methods for pattern recognition
Information Sciences: an International Journal
Information Sciences: an International Journal
Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Vision based ego-motion estimation for robot systems by type-2 fuzzy sets
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
An interval type-2 fuzzy PCM algorithm for pattern recognition
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A recurrent self-evolving interval type-2 fuzzy neural network for dynamic system processing
IEEE Transactions on Fuzzy Systems
On the stability of interval type-2 TSK fuzzy logic control systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Fuzzy clustering with hedge algebra
Proceedings of the 2010 Symposium on Information and Communication Technology
Decision making with imprecise parameters
International Journal of Approximate Reasoning
Applying I-Fuzzy Partitions to Represent Sets of Fuzzy Partitions
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
I-fuzzy partitions for representing clustering uncertainties
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
A type-2 fuzzy wavelet neural network for time series prediction
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
A fuzzy rule-based classification system using interval type-2 fuzzy sets
IUKM'11 Proceedings of the 2011 international conference on Integrated uncertainty in knowledge modelling and decision making
Information Sciences: an International Journal
A new fuzzy segmentation approach based on S-FCM type 2 using LBP-GCO features
Image Communication
Approach to image segmentation based on interval type-2 fuzzy subtractive clustering
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
The range of the value for the fuzzifier of the fuzzy c-means algorithm
Pattern Recognition Letters
A 2uFunction representation for non-uniform type-2 fuzzy sets: Theory and design
International Journal of Approximate Reasoning
Overview of Type-2 Fuzzy Logic Systems
International Journal of Fuzzy System Applications
A new indirect approach to the type-2 fuzzy systems modeling and design
Information Sciences: an International Journal
A modified interval type-2 fuzzy C-means algorithm with application in MR image segmentation
Pattern Recognition Letters
Enhanced interval type-2 fuzzy c-means algorithm with improved initial center
Pattern Recognition Letters
Tourism demand forecasting using novel hybrid system
Expert Systems with Applications: An International Journal
International Journal of Hybrid Intelligent Systems
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In many pattern recognition applications, it may be impossible in most cases to obtain perfect knowledge or information for a given pattern set. Uncertain information can create imperfect expressions for pattern sets in various pattern recognition algorithms. Therefore, various types of uncertainty may be taken into account when performing several pattern recognition methods. When one performs clustering with fuzzy sets, fuzzy membership values express assignment availability of patterns for clusters. However, when one assigns fuzzy memberships to a pattern set, imperfect information for a pattern set involves uncertainty which exist in the various parameters that are used in fuzzy membership assignment. When one encounters fuzzy clustering, fuzzy membership design includes various uncertainties (e.g., distance measure, fuzzifier, prototypes, etc.). In this paper, we focus on the uncertainty associated with the fuzzifier parameter m that controls the amount of fuzziness of the final C-partition in the fuzzy C-means (FCM) algorithm. To design and manage uncertainty for fuzzifier m, we extend a pattern set to interval type-2 fuzzy sets using two fuzzifiers m1 and m2 which creates a footprint of uncertainty (FOU) for the fuzzifier m. Then, we incorporate this interval type-2 fuzzy set into FCM to observe the effect of managing uncertainty from the two fuzzifiers. We also provide some solutions to type-reduction and defuzzification (i.e., cluster center updating and hard-partitioning) in FCM. Several experimental results are given to show the validity of our method