Learning image semantics from users relevance feedback
Proceedings of the 12th annual ACM international conference on Multimedia
Computational Statistics & Data Analysis
IEEE Transactions on Fuzzy Systems
PCA-Guided k-Means with Variable Weighting and Its Application to Document Clustering
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Fuzzy Sets and Systems
Fuzzy PCA-guided robust k-means clustering
IEEE Transactions on Fuzzy Systems
A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data
Expert Systems with Applications: An International Journal
Local subspace learning by extended fuzzy c-medoids clustering
International Journal of Knowledge Engineering and Soft Data Paradigms
Quantification of multivariate categorical data considering clusters of items and individuals
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Alternative fuzzy c-lines and local principal component extraction
International Journal of Knowledge Engineering and Soft Data Paradigms
Fuzzy data mining: a literature survey and classification framework
International Journal of Networking and Virtual Organisations
Fuzzy Cluster Validation Based on Fuzzy PCA-Guided Procedure
International Journal of Fuzzy System Applications
Improving project-profit prediction using a two-stage forecasting system
Computers and Industrial Engineering
Tourism demand forecasting using novel hybrid system
Expert Systems with Applications: An International Journal
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In this paper, we propose two methods for partitioning an incomplete data set with missing values into several linear fuzzy clusters by extracting local principal components. One is an extension of fuzzy c-varieties clustering that can be regarded as the algorithm for the local principal component analysis of fuzzy covariance matrices. The other is a simultaneous application of fuzzy clustering and principal component analysis of fuzzy correlation matrices. Both methods estimate prototypes ignoring only missing values and they need no preprocessing of data such as the elimination of samples with missing values or the imputation of missing elements. Numerical examples show that the methods provide useful tools for interpretation of the local structures of a database.