Interval type-2 approach to kernel possibilistic C-means clustering

Raza, Muhammad Amjad; Rhee, Frank Chung-Hoon

doi:10.1109/fuzz-ieee.2012.6251233

Cited by 14 publications

(6 citation statements)

References 22 publications

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“…It is known that the synthesis of FCM and T2FS gives more room to handle the uncertainties of clustering caused by noisy environment. These hybrid algorithms include the general type-2 FCM [8], Interval Type-2 FCM (IT2-FCM) [9], kernel IT2-FCM [10], interval type-2 fuzzy c-regression clustering [11], interval type-2 possibilistic c-means clustering [12] [13], interval type-2 relative entropy FCM [14], particle swarm optimization based IT2-FCM [15], interval-valued fuzzy setbased collaborative fuzzy clustering [16]. This T2FS based algorithms have been successfully applied to areas like image processing, time series prediction and others.…”

Section: Background Theory a Reserch Trendmentioning

confidence: 99%

Data Clustering Method Using Efficient Fuzzifier Values Derivation

Cho

Joo

2020

IEEE Access

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The Type-2 fuzzy set (T2 FS) is widely used for efficient control uncertainties, such as noise sensitivity in the fuzzy set. In addition, unsupervised machine learning requires a clustering parameter value in advance, and may affect clustering performance according to prior information such as the number and size of clusters. In this case, the fuzzifier value m to be applied is the most important factor in improving the accuracy of data. Therefore, in this paper, we intend to perform clustering to automatically acquire the determination of m1 and m2 values that depended on existing repeated experiments. To this end, in order to increase efficiency on deriving appropriate fuzzifier value, we used the Interval type-2 possibilistic fuzzy Cmeans (IT2PFCM), clustering method to classify a given pattern. In Efficient IT2PFCM method, used for clustering, we propose an algorithm that derives suitable fuzzifier values for each data. These values also extract information from each data point through the histogram approach and Gaussian Curve Fitting method. Using the extracted information, two adaptive fuzzifier value m1 and m2 are determined. Obtained values apply the new lowest and highest membership values. In addition, it is possible to form an appropriate fuzzy area on each cluster by only taking advantage of the characteristics of IT2PFCM, which reduces uncertainty. This doesn't only improve the accuracy of clustering of measured sensor data, but can also be used without additional procedures such as data labeling or the provision of prior information. It is also efficient at monitoring numerous sensors, managing and verifying sensor data collected in real time such as smart cities. Eventually, in this study, the proposed method is to improve IT2PFCM performance on accurate and quick clustering of large amount of complex data such as Internet of Things (IoT).

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Section: Background Theory a Reserch Trendmentioning

confidence: 99%

Data Clustering Method Using Efficient Fuzzifier Values Derivation

Cho

Joo

2020

IEEE Access

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“…Then, it adjusts the coefficients of the kernels and combines them in the feature space to produce a new kernel. Other kernel clustering algorithms, based on type 2 fuzzy sets, include [61,62]. A kernel intuitionistic FCM clustering algorithm (KIFCM) was proposed in [63].…”

Section: Other Fuzzy Kernel Clustering Algorithmsmentioning

confidence: 99%

An Overview of Unsupervised and Semi-Supervised Fuzzy Kernel Clustering

Frigui

Bchir²,

Baili³

2013

International Journal of Fuzzy Logic and Intelligent Systems

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For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-type objective function. We highlight the advantages and disadvantages of each method. In addition to the completely unsupervised algorithms, we also provide an overview of some semi-supervised fuzzy kernel clustering algorithms. These algorithms use partial supervision information to guide the optimization process and avoid local minima. We also provide an overview of the different approaches that have been used to extend kernel clustering to handle very large data sets.

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“…In the first one, fuzzy theories does not involve as a necessary consequence positive definite kernels (or vice versa), some specific fuzzy techniques and positive definite kernels are used to solve particular problems. Some works in clustering [11]- [13], classification problems with outliers or noises [14], feature extraction [15], discriminant analysis [16] among others are in this group.…”

Section: A Kernel Methods and Fuzzy Rulesmentioning

confidence: 99%

Kernel functions in Takagi-Sugeno-Kang fuzzy system with nonsingleton fuzzy input

Guevara

Hirata

Canu

2013

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

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Abstract-Algorithms for supervised classification problems usually does not consider imprecise data, e.g., observed data whose samples can be represented by a collection of intervals, histograms, list of values, fuzzy sets among others. Fuzzy theory is a naturally choice for imprecise data. On the other hand, the state of the art techniques such a kernel methods are still a natural choice for supervised classification problems because of its robustness. Under some assumptions, Takagi-Sugeno-Kang (TSK) fuzzy system rules are equivalent to positive definite kernels (PDK) but such relationship was given considering only singleton fuzzy sets to model crisp input. Imprecise data are better modeled by membership functions of nonsingleton fuzzy sets but the relationship among nonsingleton fuzzy input, TSK fuzzy systems and PDK was up to our knowledge unknown. In this work, we study such relationship. We formulate an extension of TSK fuzzy systems to deal with nonsingleton fuzzy input and then we show that a new class of PDK are derived from it. We give three examples of nonsingleton TSK kernels which are close related to Vapnik's vicinal kernels. Also, based on those TSK induced kernels and on the concept of distance substitution kernels, we formulate two PDK for interval data. Potential applications for the proposed kernels are pattern recognition problems with imprecise data. Experiments conducted with interval datasets show better performance than state of the art approaches.

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Interval type-2 approach to kernel possibilistic C-means clustering

Cited by 14 publications

References 22 publications

Data Clustering Method Using Efficient Fuzzifier Values Derivation

Data Clustering Method Using Efficient Fuzzifier Values Derivation

An Overview of Unsupervised and Semi-Supervised Fuzzy Kernel Clustering

Kernel functions in Takagi-Sugeno-Kang fuzzy system with nonsingleton fuzzy input

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