Time-Series Prediction with Cloud Models in DMKD
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Computing with words and its relationships with fuzzistics
Information Sciences: an International Journal
Spatial Clustering Method Based on Cloud Model
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Artificial Intelligence with Uncertainty
Artificial Intelligence with Uncertainty
A new cognitive model: Cloud model
International Journal of Intelligent Systems
Computing with words in decision making: foundations, trends and prospects
Fuzzy Optimization and Decision Making
Computing with words for hierarchical decision making applied to evaluating a weapon system
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Perceptual Computing: Aiding People in Making Subjective Judgments
Perceptual Computing: Aiding People in Making Subjective Judgments
On the retranslation process in Zadeh's paradigm of computing with words
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
A 2-tuple fuzzy linguistic representation model for computing with words
IEEE Transactions on Fuzzy Systems
A new version of 2-tuple fuzzy linguistic representation model for computing with words
IEEE Transactions on Fuzzy Systems
Encoding Words Into Interval Type-2 Fuzzy Sets Using an Interval Approach
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
Image segmentation based on histogram analysis utilizing the cloud model
Computers & Mathematics with Applications
How to handle uncertainties in AHP: The Cloud Delphi hierarchical analysis
Information Sciences: an International Journal
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When constructing the model of a word by collecting interval-valued data from a group of individuals, both interpersonal and intrapersonal uncertainties coexist. Similar to the interval type-2 fuzzy set (IT2 FS) used in the enhanced interval approach (EIA), the Cloud model characterized by only three parameters can manage both uncertainties. Thus, based on the Cloud model, this paper proposes a new representation model for a word from interval-valued data. In our proposed method, firstly, the collected data intervals are preprocessed to remove the bad ones. Secondly, the fuzzy statistical method is used to compute the histogram of the surviving intervals. Then, the generated histogram is fitted by a Gaussian curve function. Finally, the fitted results are mapped into the parameters of a Cloud model to obtain the parametric model for a word. Compared with eight or nine parameters needed by an IT2 FS, only three parameters are needed to represent a Cloud model. Therefore, we develop a much more parsimonious parametric model for a word based on the Cloud model. Generally a simpler representation model with less parameters usually means less computations and memory requirements in applications. Moreover, the comparison experiments with the recent EIA show that, our proposed method can not only obtain much thinner footprints of uncertainty (FOUs) but also capture sufficient uncertainties of words.