Fuzzy cluster analysis with missing values

Timm, Heiko; Kruse, Rudolf

doi:10.1109/nafips.1998.715573

Cited by 19 publications

(17 citation statements)

References 10 publications

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“…Replacing missing values can potentially present disadvantages [7], [8], and is used if the missing values occur rarely or if they can be imputed with a high reliability. Widely used replacement methods use the variables mean, median or the most probable value as a replacement [1], [5].…”

Section: Introductionmentioning

confidence: 99%

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A new approach to dealing with missing values in data-driven fuzzy modeling

Almeida

Kaymak

Sousa

2010

International Conference on Fuzzy Systems

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da, J. M. (2010). A new approach to dealing with missing values in data-driven fuzzy modeling. In Proceedings of the World Congress on Computational Intelligence (WCCI 2010), July 18-23, 2010, Barcelona, Spain (pp. 312-318). Piscataway: Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/FUZZY.2010 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract-Real word data sets often contain many missing elements. Most algorithms that automatically develop a rulebased model are not well suited to deal with incomplete data. The usual technique is to disregard the missing values or substitute them by a best guess estimate, which can bias the results. In this paper we propose a new method for estimating the parameters of a Takagi-Sugeno fuzzy model in the presence of incomplete data. We also propose an inference mechanism that can deal with the incomplete data. The presented method has the added advantage that it does not require imputation or iterative guess-estimate of the missing values. This methodology is applied to fuzzy modeling of a classification and regression problem. The performance of the obtained models are comparable with the results obtained when using a complete data set.

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Section: Introductionmentioning

confidence: 99%

“…In this work, two of the approaches iteratively find the best guess estimate of the missing data, and another approach uses the partial distance strategy (PDS). This fuzzy cluster analysis using the partial distance strategy is also discussed in [12].…”

Section: Introductionmentioning

confidence: 99%

A new approach to dealing with missing values in data-driven fuzzy modeling

Almeida

Kaymak

Sousa

2010

International Conference on Fuzzy Systems

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“…Another simple approach is to ignore the missing values and calculate the distances from the remaining coordinates. Timm et al [11] also proposed similar techniques and reported that the simple approach of ignoring the missing values gave fuzzier membership assignments than the strategy of replacing the missing values with the averages. Other observations such as the convergence property and the initialization problem are discussed in [12].…”

Section: Introductionmentioning

confidence: 99%

Linear Fuzzy Clustering Techniques With Missing Values and Their Application to Local Principal Component Analysis

Honda

Ichihashi

2004

IEEE Trans. Fuzzy Syst.

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“…Partial Distance Strategy (PDS) [32,11,31] clustering algorithm uses the partial distance. This approach is similar to FCM algorithm [7] and has two differences:…”

Section: Partial Distance Strategymentioning

confidence: 99%

“…The improved fuzzy c-means (IFCM) [32] imputes the missing values iteratively. The clusters are elaborated with full data examples, then the missing values are imputed with weighted mean of values of missing attributes from elaborated clusters' centers:…”

Section: Improved Fuzzy C-meansmentioning

confidence: 99%