Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Expanding self-organizing map for data visualization and cluster analysis
Information Sciences: an International Journal - Special issue: Soft computing data mining
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature selection with conditional mutual information maximin in text categorization
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Mining risk patterns in medical data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A delivery framework for health data mining and analytics
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Scalable Model-Based Clustering for Large Databases Based on Data Summarization
IEEE Transactions on Pattern Analysis and Machine Intelligence
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Automatic feature selection for classification of health data
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Representing association classification rules mined from health data
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Analysis of Moving Patterns of Moving Objects with the Proposed Framework
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Association analysis of location tracking data for various telematics services
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Proceedings of the 6th Euro American Conference on Telematics and Information Systems
Information Technology and Management
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The purpose of this study is to demonstrate the benefit of using common data mining techniques on survey data where statistical analysis is routinely applied. The statistical survey is commonly used to collect quantitative information about an item in a population. Statistical analysis is usually carried out on survey data to test hypothesis. We report in this paper an application of data mining methodologies to breast feeding survey data which have been conducted and analysed by statisticians. The purpose of the research is to study the factors leading to deciding whether or not to breast feed a new born baby. Various data mining methods are applied to the data. Feature or variable selection is conducted to select the most discriminative and least redundant features using an information theory based method and a statistical approach. Decision tree and regression approaches are tested on classification tasks using features selected. Risk pattern mining method is also applied to identify groups with high risk of not breast feeding. The success of data mining in this study suggests that using data mining approaches will be applicable to other similar survey data. The data mining methods, which enable a search for hypotheses, may be used as a complementary survey data analysis tool to traditional statistical analysis.