Statistical analysis with missing data
Statistical analysis with missing data
Data mining: concepts and techniques
Data mining: concepts and techniques
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy c-means clustering of incomplete data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Data Envelopment Analysis of clinics with sparse data: Fuzzy clustering approach
Computers and Industrial Engineering
Consensus strategy for clustering using RC-images
Pattern Recognition
Clustering with Missing Values
Fundamenta Informaticae
| Hi-index | 0.00 |
Clustering algorithms are used to identify groups of similar data objects within large data sets. Since traditional clustering methods were developed to analyse complete data sets, they cannot be applied to many practical problems, e.g. on incomplete data. Approaches proposed for adapting clustering algorithms for dealing with missing values work well on uniformly distributed data sets. But in real world applications clusters are generally differently sized. In this paper we present an extension for existing fuzzy c-means clustering algorithms for incomplete data, which uses the information about the dispersion of clusters. In experiments on artificial and real data sets we show that our approach outperforms other clustering methods for incomplete data.