C4.5: programs for machine learning
C4.5: programs for machine learning
Comparing connectionist and symbolic learning methods
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
Feature minimization within decision trees
Computational Optimization and Applications
Data mining: concepts and techniques
Data mining: concepts and techniques
Building Data Mining Applications for CRM
Building Data Mining Applications for CRM
Data Mining for Scientific and Engineering Applications
Data Mining for Scientific and Engineering Applications
Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
ECML '93 Proceedings of the European Conference on Machine Learning
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
Secure distributed data-mining and its application to large-scale network measurements
ACM SIGCOMM Computer Communication Review
Mining Rules for Risk Factors on Blood Stream Infection in Hospital Information System
BIBM '07 Proceedings of the 2007 IEEE International Conference on Bioinformatics and Biomedicine
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A tutorial about diagnostic methodology with dementia data
International Journal of Data Analysis Techniques and Strategies
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Statistical data mining is one of the popular research fields of exploring valuable information from the large number of collected data. Applying statistical data mining techniques in several fields like medicine, bioinformatics, business, and bacteriology can be beneficial. Among them, bacteriology is one of the promising fields where statistical data mining is hardly ever used. The main purpose of this paper is to demonstrate the contribution of statistical data mining in the field of bacteriology. The research problem named as bacterial identification from bacteriology that we handle in this paper is a special kind of classification problem of statistical data mining where the only single representation of every class is present in the dataset. After studying this research problem, this paper proposes a novel statistical data mining approach using the decision tree technique in bacterial identification with better performance. The experimental results show significantly less number of biochemical tests are needed in bacterial identification using this proposed approach than the conventional approach that is being followed currently in the biochemical laboratory. Thus, the proposed approach not only benefits microbiologists, but it also improves the traditional approach of bacterial identification by saving time, total cost, and manual labour involvements.