Statistical analysis with missing data
Statistical analysis with missing data
Algorithms for clustering data
Algorithms for clustering data
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer-assisted reasoning in cluster analysis
Computer-assisted reasoning in cluster analysis
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Information Retrieval
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Cluster validity methods: part I
ACM SIGMOD Record
Clustering validity checking methods: part II
ACM SIGMOD Record
Techniques for Dealing with Missing Values in Classification
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
Data analysis with fuzzy clustering methods
Computational Statistics & Data Analysis
Fuzzy c-means clustering of incomplete data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Exploratory data analysis is often necessary to evaluate potential hypotheses for subsequent studies such as grouping the data in clusters. In real data sets the occurrence of incompleteness is very common. We propose a method that tolerates missing values for fuzzy clustering using resampling (bootstrapping) and cluster stability analysis. The quality of classification is based on the measures like F1 and Hubert. The central idea is to compare a reference cluster with many clusters from sub-samples of the original data set. The results demonstrate that our method is capable of identifying relevant partitions even with high presence of missing values.