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» K-means Clustering for Classifying Unlabelled MRI Data
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DICTA
2007
13 years 6 months ago
K-means Clustering for Classifying Unlabelled MRI Data
Texture analysis of the liver for the diagnosis of cirrhosis is usually region-of-interest (ROI) based. Integrity of the label of ROI data may be a problem due to sampling. This p...
Gobert N. Lee, Hiroshi Fujita
ICDM
2003
IEEE
210views Data Mining» more  ICDM 2003»
13 years 10 months ago
CBC: Clustering Based Text Classification Requiring Minimal Labeled Data
Semi-supervised learning methods construct classifiers using both labeled and unlabeled training data samples. While unlabeled data samples can help to improve the accuracy of trai...
Hua-Jun Zeng, Xuanhui Wang, Zheng Chen, Hongjun Lu...
KDD
2002
ACM
179views Data Mining» more  KDD 2002»
14 years 5 months ago
Combining clustering and co-training to enhance text classification using unlabelled data
In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for...
Bhavani Raskutti, Herman L. Ferrá, Adam Kow...
TSMC
2008
189views more  TSMC 2008»
13 years 4 months ago
Automatic Clustering Using an Improved Differential Evolution Algorithm
Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. This paper describes an application of DE to the aut...
Swagatam Das, Ajith Abraham, Amit Konar
KDD
2002
ACM
147views Data Mining» more  KDD 2002»
14 years 5 months ago
A parallel learning algorithm for text classification
Text classification is the process of classifying documents into predefined categories based on their content. Existing supervised learning algorithms to automatically classify te...
Canasai Kruengkrai, Chuleerat Jaruskulchai