Switching regression models and fuzzy clustering

Hathaway, Richard J.; Bezdek, James C.

doi:10.1109/91.236552

Cited by 456 publications

(208 citation statements)

References 20 publications

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“…The premise parameters can be easily obtained using μ ik . 31 The fuzzy sets centers v ik and the standard deviations σ ij are calculated as follows:…”

Section: Identification Of Premise and Consequent Parametersmentioning

confidence: 99%

A fuzzy C‐regression model algorithm using a new PSO algorithm

Adel

Soltani

Châari

2017

Adaptive Control & Signal

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SummaryIn this paper, a new methodology is introduced for the identification of the parameters of the multiple-input-multiple-output local linear Takagi-Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi-Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature. KEYWORDSchaos adaptive particle swarm optimization, fuzzy C-regression model clustering algorithm, identification, multiple-input multiple-output, Takagi-Sugeno fuzzy models

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“…The premise parameters can be easily obtained using μ ik . 31 The fuzzy sets centers v ik and the standard deviations σ ij are calculated as follows:…”

Section: Identification Of Premise and Consequent Parametersmentioning

confidence: 99%

A fuzzy C‐regression model algorithm using a new PSO algorithm

Adel

Soltani

Châari

2017

Adaptive Control & Signal

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“…The larger σ is, the higher variation of numerical attribute values will be. The generation of the numerical attribute values of such synthetic data sets is proposed in [9]. To obtain the average performance, we generated 100 test cases for each type of data sets.…”

Section: Synthetic Data Setsmentioning

confidence: 99%

“…Here we consider quadratic regression models. Similar to the linear case, we constructed four types of synthetic data sets, namely Sample A, Sample B, Sample C and Sample D (see the generation of these data sets in [9]). For simplicity of discussion, we only test for quadratic equations in several settings.…”

Section: Synthetic Data Setsmentioning

confidence: 99%

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A semi-supervised regression model for mixed numerical and categorical variables

Chan

et al. 2007

Pattern Recognition

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In this paper, we develop a semi-supervised regression algorithm to analyze data sets which contain both categorical and numerical attributes. This algorithm partitions the data sets into several clusters and at the same time fits a multivariate regression model to each cluster. This framework allows one to incorporate both multivariate regression models for numerical variables (supervised learning methods) and k-modes clustering algorithms for categorical variables (unsupervised learning methods). The estimates of regression models and k-modes parameters can be obtained simultaneously by minimizing a function which is the weighted sum of the least squares errors in the multivariate regression models and the dissimilarity measures among the categorical variables. Both synthetic and real data sets are presented to demonstrate the effectiveness of the proposed method.

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“…The use of procedures based on likelihoods is also useful in clusterwise linear regression problems. Instead of detecting clusters just around centroids, it is often interesting to detect clusters around linear structures [15,21,29] (hard clustering) and [14,16] (fuzzy clustering).…”

Section: Introductionmentioning

confidence: 99%

Robust Fuzzy Clustering via Trimming and Constraints

Dotto

Farcomeni

García-Escudero

et al. 2016

Advances in Intelligent Systems and Computing

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A methodology for robust fuzzy clustering is proposed. This methodology can be widely applied in very different statistical problems given that it is based on probability likelihoods. Robustness is achieved by trimming a fixed proportion of "most outlying" observations which are indeed self-determined by the data set at hand. Constraints on the clusters' scatters are also needed to get mathematically well-defined problems and to avoid the detection of non-interesting spurious clusters. The main lines for computationally feasible algorithms are provided and some simple guidelines about how to choose tuning parameters are briefly outlined. The proposed methodology is illustrated through two applications. The first one is aimed at heterogeneously clustering under multivariate normal assumptions and the second one migh be useful in fuzzy clusterwise linear regression problems.

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Switching regression models and fuzzy clustering

Cited by 456 publications

References 20 publications

A fuzzy C‐regression model algorithm using a new PSO algorithm

A fuzzy C‐regression model algorithm using a new PSO algorithm

A semi-supervised regression model for mixed numerical and categorical variables

Robust Fuzzy Clustering via Trimming and Constraints

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