Fuzzy cluster analysis with missing values
Published 27 November 2002
Heiko Timm, Rudolf Kruse
Citations22
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Methods of fuzzy cluster analysis can be extended in such a way that they are able to process datasets with missing values to propose a new approach for reducing the influence of imputations.
Abstract
The paper shows how methods of fuzzy cluster analysis can be extended in such a way that they are able to process datasets with missing values. We study several imputation methods and propose a new approach for reducing the influence of imputations.
Keywords
Computer Science
IEEE Transactions on Fuzzy SystemsA possibilistic approach to clustering
2,480 Citations1993Raghu Krishnapuram, James M. Keller
An appropriate objective function whose minimum will characterize a good possibilistic partition of the data is constructed, and the membership and prototype update equations are derived from necessary conditions for minimization of the criterion function.
Journal of the Operational Research SocietyFuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition
929 Citations2000V. J. Rayward‐Smith, Frank Höppner +3 more
This paper presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and therefore expensive process of rule generation and estimation in the context of cluster dynamics.
IEEE Transactions on Fuzzy SystemsRobust clustering methods: a unified view
708 Citations1997Rajesh N. Davé, Raghu Krishnapuram
This paper analyzes several popular robust clustering methods and concludes that they have much in common, establishing a connection between fuzzy set theory and robust statistics, and pointing out the similarities between robust clusters methods and statistical methods.
Proceedings of 6th International Fuzzy Systems ConferenceA mixed c-means clustering model
341 Citations2002Nikhil R. Pal, K PAL +1 more
F fuzzy-possibilistic c-means is proposed, and it is shown that FPCM solves the noise sensitivity defect of FCM, and also overcomes the coincident clusters problem of PCM.
European Journal of Operational ResearchHandbook of statistics 2: Classification, pattern recognition and reduction of dimensionality
112 Citations1984Antonio Bellacicco
A noise-resistant fuzzy c means algorithm for clustering
84 Citations2002Krishna Chintalapudi, Moshe Kam
The new credibilistic fuzzy c means (CFCM) algorithm is proposed, which uses a new variable, credibility of a vector, which measures the typicality of the vector to the whole data set, and generates centroids which are less sensitive to outliers than other techniques, and closer to theCentroids generated when the outliers are artificially removed.
Lecture notes in computer scienceTechniques for dealing with missing values in classification
79 Citations1997W. Z. Liu, A. P. White +2 more
The technique of dynamic path generation is described in the context of tree-based classification methods and the waste of data which can result from casewise deletion of missing values in statistical algorithms is discussed and alternatives proposed.