Classification by minimum-message-length inference
ICCI'90 Proceedings of the international conference on Advances in computing and information
Predicting prostate cancer recurrence via maximizing the concordance index
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Identification of patients with congestive heart failure using a binary classifier: a case study
BioMed '03 Proceedings of the ACL 2003 workshop on Natural language processing in biomedicine - Volume 13
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Machine Learning and Data Mining: Introduction to Principles and Algorithms
Machine Learning and Data Mining: Introduction to Principles and Algorithms
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Probing the existence of medium pulmonary crackles via model-based clustering
Computers in Biology and Medicine
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In this paper we present a machine learning approach to mining a questionnaire database, which is part of the medical record, of patients suffering from a degree of autism. One of the most important components of this approach is the selection of features for identifying subtypes of autism.. Different sets of features have been chosen as input to experiments that ran using various machine learning algorithms. Results of such experiments are evaluated to determine their effectiveness in clustering patients' data for further classification purposes. The ultimate goal of our research is to provide an approach for mining autistic patients' data for categorizing the Autism Spectrum Disorder(ASD)to better understand this currently wide spread and complex medical condition.