Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Finding Association Rules That Trade Support Optimally against Confidence
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining
Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Exploratory Quantitative Contrast Set Mining: A Discretization Approach
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Using classification to evaluate the output of confidence-based association rule mining
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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In this study a new approach to generate association rules on numeric data is proposed. It has been observed that equal binning techniques are not always useful to convert numerical data into categorical data, specifically in medical data. The proposed approach utilise a modified equal width binning interval technique to discretise continuous valued attributes to nominal based on opinion taken from medical experts. Approximate width of the desired intervals is chosen based on the advice given by medical experts and is given as an input to the model. Apriori algorithm usually used for the market basket analysis is used to generate rules on Pima Indian diabetes data. The study compares the quality of different association rule mining approaches for classification. The proposed approach utilises standard Apriori and predictive Apriori algorithms to generate association rules and highlights the importance of the often neglected pre-processing steps in data mining process. The proposed approach can help doctors to explore their data in a better way.