Analysis and efficient implementation of a linguistic fuzzy c-means

Auephanwiriyakul, Sansanee; Keller, J.M.

doi:10.1109/tfuzz.2002.803492

Cited by 43 publications

(19 citation statements)

References 29 publications

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“…Fuzzy C-means (FCM) allows one data point to belong to two or more clusters [14], [17]. It provides a method that groups data points in multidimensional space into a specific number of clusters.…”

Section: ) Fuzzy C-means Methodmentioning

confidence: 99%

Identification of Key Variables Using Fuzzy Average With Fuzzy Cluster Distribution

Hou

Zurada

Karwowski

et al. 2007

IEEE Trans. Fuzzy Syst.

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Abstract-Identification of the significance of input variables is very important for complex systems with high-dimensional input space. In this paper, a method using fuzzy average with fuzzy cluster distribution is proposed. To avoid the interference of different distributions of the sampling data, the distribution of fuzzy clusters in the sampling data is considered, instead of the original data set. To discover the input-output relationship, the methods of fuzzy rules and fuzzy C-means are first used to partition the original sampling data set into fuzzy clusters. A new data set with the same distribution of the fuzzy clusters is produced. The fuzzy average method is then applied to the new data set. By doing so, the interference of distribution of the original sampling data is removed. This method is straightforward and computationally easy. The performance is tested on both benchmark data and real-world data.

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Section: ) Fuzzy C-means Methodmentioning

confidence: 99%

Identification of Key Variables Using Fuzzy Average With Fuzzy Cluster Distribution

Hou

Zurada

Karwowski

et al. 2007

IEEE Trans. Fuzzy Syst.

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“…Körner (2000), Montenegro et al (2001), Gil et al (2006), González-Rodríguez et al (2012), Ramos-Guajardo et al (2010), Ramos-Guajardo and Lubiano (2012) and Lubiano et al (2016b) Fuzzy estimates of location of random fuzzy numbers; robustness Lubiano and Gil (1999) and Sinova et al (2016) Statistical comparison of fuzzy scale with other imprecise-valued scales De la Rosa de Sáa et al (2016), Gil et al (2015) and Lubiano et al (2016aLubiano et al ( , 2017 Fuzzy inequality Gil et al (1998) Discriminant analysis Colubi et al (2011) Cluster analysis Hathaway et al (1996), Pedrycz et al (1998), Auephanwiriyakul and Keller (2002), D'Urso (2007) and Coppi et al (2012) Regression analysis Celminš (1987), Diamond (1988), Näther and Albrecht (1990) …”

Section: Additional Related Literaturementioning

confidence: 99%

Fuzzy data analysis and classification

D’Urso

Alvarez

2017

Adv Data Anal Classif

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In analyzing and classifying data from a statistical perspective, fuzzy sets and logic have become a valuable tool either to model and handle imprecise data or to establish flexible techniques to deal with precise data.From the very beginning of his 52 years-old theory, Professor Zadeh highlighted that "Probability theory/statistics and fuzzy logic should be viewed as complementary rather than competitive," and he anticipated and encouraged the materialization of such a complementarity. Nowadays, this assertion is a reality, as shown by the many related papers, specialized conferences, special sessions and tracks in general conferences, and so on.This special issue started in 2015, with the 50th anniversary of the seminal paper on fuzzy sets by Zadeh (1965), aiming to collect a sample of research papers about the current trends on the combination of fuzzy sets/logic and data analysis/classification. When this special issue was almost ready for publication, Zadeh unfortunately passed away at age 96 (February 4, 1921-September 6, 2017. We wish this special issue to be dedicated to Professor Zadeh, as a modest part of the many tributes that he will receive, and intending to show that fuzzy sets/logic and data analysis/classification can certainly work in synergy.

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“…Genetic approaches are also used [46], but the most widely used technique to generate parsimonious fuzzy models is cluster analysis, see e.g. [117][118][119][120][121][122][123][124][125][126]50,49,[127][128][129].…”

Section: Number Of Rules (Compactness)mentioning

confidence: 99%