Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparative study of clustering methods
Future Generation Computer Systems - Special double issue on data mining
On post-clustering evaluation and modification
Pattern Recognition Letters
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Relationship-based clustering and cluster ensembles for high-dimensional data mining
Relationship-based clustering and cluster ensembles for high-dimensional data mining
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of clustering methods for clinical databases
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A quantitative comparison of functional MRI cluster analysis
Artificial Intelligence in Medicine
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Clustering stability-based feature selection for unsupervised texture classification
Machine Graphics & Vision International Journal
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This paper focuses on the problem of dimensionality reduction for objects described by a large number of features. The emphasis is put on the issues of grouping unlabelled data sets, where information about class-membership of observations is unavailable. Commonly used feature extraction methods for unsupervised classification tasks (such as PCA) are not applicable when information necessary for partitioning of the data set is not represented by the data structure as a whole, but is hidden in a limited number of features only. Thus, we propose a novel technique for choosing the best discriminative data features in an unsupervised manner. Our approach is based on data clustering and on clustering quality measures. The method is straightforward but proved perceptive and efficient. Since the research was primarily motivated by the specific problem of classifying MRI data, performance of the constructed algorithm is studied in application to textured image analysis.