An on-line agglomerative clustering method for nonstationary data
Neural Computation
Stability-based validation of clustering solutions
Neural Computation
A Modified K-Means Algorithm for Circular Invariant Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Genetic Algorithm Using Hyper-Quadtrees for Low-Dimensional K-means Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
2005 Special Issue: Efficient streaming text clustering
Neural Networks - 2005 Special issue: IJCNN 2005
Validity-guided (re)clustering with applications to image segmentation
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Image Processing
Bayesian clustering of flow cytometry data for the diagnosis of B-Chronic Lymphocytic Leukemia
Journal of Biomedical Informatics
Computers in Biology and Medicine
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Background: Flow cytometry produces large multi-dimensional datasets of the physical and molecular characteristics of individual cells. The objective of this study was to simplify the cytometry datasets by arranging or clustering ''objects'' (cells) into a smaller number of relatively homogeneous groups (clusters) on the basis of interobject similarities and dissimilarities. Results: The algorithm was designed to be driven by histogram features; that is, the relevant single parameter histogram features were used to guide multidimensional k-means clustering without an a priori estimate of cluster number. To test this approach, we simulated cell-derived datasets using protein-coated microspheres (artificial ''cells''). The microspheres were constructed to provide 119 populations in 40 samples. The feature-guided (FG) approach accurately identified 100% of the predetermined cluster combinations. In contrast, an approach based on the partition index (PI) cluster validity measure accurately identified 83.2% of the clusters. Direct comparisons of the two methods indicated that the FG method was significantly more accurate than PI in identifying both the number of clusters and the number of objects within the clusters (p