On similarity and inclusion measures between type-2 fuzzy sets with an application to clustering

Yang, Miin-Shen; Lin, Der-Chen

doi:10.1016/j.camwa.2008.10.028

Cited by 95 publications

(56 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition Yang worked on similarity metrics of type-2 fuzzy clustering algorithms on fuzzy datasets [23][24][25][26]. In these studies, Yang redefined new similarity metrics based on union maximum.…”

Section: Introductionmentioning

confidence: 99%

Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms

Nouri

Akbarzadeh²,

Rowhanimanesh³

2014

IJIIS

View full text Add to dashboard Cite

Abstract:In several papers, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification results. A strong clustered dataset as input to classification algorithms can significantly improve the computation time. This can be particularly useful in "Big Data" where computation time is equally or more important than accuracy. However, there is a trade-off between computation time (speed) and accuracy among clustering algorithms. Specifically, general type-2 fuzzy c-means (GT2 FCM) is considered to be a highly accurate clustering approach, but it is computationally intensive. To improve its computation time we propose a hybrid clustering algorithm called KFGT2FCM that combines GT2 FCM with two fast algorithms k-means and Fuzzy C-means algorithm for input data preprocessing of classification algorithms. The proposed algorithm shows improved computation time when compared with GT2 FCM on five benchmarks from university of California Irvine (UCI) library.

show abstract

Section: Introductionmentioning

confidence: 99%

Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms

Nouri

Akbarzadeh²,

Rowhanimanesh³

2014

IJIIS

View full text Add to dashboard Cite

show abstract

“…Entropy measures have wide applications in various fields, such as pattern recognition, clustering analysis, approximate reasoning, image processing, and decision making [16][17][18][19]. But little research has been done about hesitant fuzzy entropy measure.…”

Section: Preliminary Knowledgementioning

confidence: 99%

Hesitant Fuzzy Multi-attribute Decision Making Method based on a New Entropy Measure

Yin¹

2017

IJHIT

View full text Add to dashboard Cite

show abstract

“…2) Similarity measures for interval and general type-2 fuzzy sets: A number of methods have been proposed for measuring the similarity of interval T2 FSs (e.g., [20], [21], [22] and [23]) and general T2 FSs (e.g., [24] and [25]). In [26] and [27] the Jaccard similarity coefficient is extended for use with interval T2 FSs.…”

Section: Similarity Measuresmentioning

confidence: 99%

Similarity based applications for data-driven concept and word models based on type-1 and type-2 fuzzy sets

Wagner

Miller

Garibaldi

2013

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

Abstract-In this paper we explore the practical application of the previously introduced approach [1] to generate fuzzy sets from interval-valued data. We demonstrate two specific example applications where we 1) generate type-1 fuzzy sets from intervalvalued survey data for both words (e.g., neutral, excellent) and concepts (e.g., ambience, food) and 2) generate zSlices based general type-2 fuzzy set valued data from multiple iterations of a survey. We highlight the need for the simultaneous rating of both concepts and words in order to maintain context (including timeliness) of the resulting models. Further, in both example applications, we demonstrate using the Jaccard similarity measure how similarity measures can be employed to both relate and attribute word models to concept models (e.g., excellent food) and compare different concepts directly for different contexts (e.g., ambience in venue A vs. ambience in venue B). We provide interpretations for the resulting word/concept models and similarity values and highlight their utility, for example, for the data-driven generation of linguistic descriptions of venues. Finally, we highlight remaining questions and challenges both in technical terms and in application terms.

show abstract

On similarity and inclusion measures between type-2 fuzzy sets with an application to clustering

Cited by 95 publications

References 36 publications

Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms

Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms

Hesitant Fuzzy Multi-attribute Decision Making Method based on a New Entropy Measure

Similarity based applications for data-driven concept and word models based on type-1 and type-2 fuzzy sets

Contact Info

Product

Resources

About