Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A survey of interestingness measures for knowledge discovery
The Knowledge Engineering Review
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Artificial Intelligence in Medicine
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In order to select the best interestingness measure appropriate for evaluating the correlation between syndrome elements and symptoms, 60 objective interestingness measures were selected from different subjects. Firstly, a hypothesis for a good measure was proposed. Based on the hypothesis, an experiment was designed to evaluate the measures. The experiment was based on the clinical record database of past dynasties including 51,186 clinical cases. The selected dataset in this study had 44,600 records. Han and Re were selected as the experimental syndrome elements. Three indicators calculated according to the distances between two syndrome elements were obtained in the experiment and were combined into one indicator. The Z score, φ-coefficient and Kappa were selected from 60 measures after the experiment. The Z score and φ- coefficient were selected according to subjective interestingness. Finally, the φ- coefficient was selected as the best measure for its low computational complexity. The method introduced in this paper may be used in other similar territories. Further research of traditional Chinese medicine can be made based on the conclusion made in this paper.