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Selecting An Appropriate interestingness measure to Evaluate the Correlation between Chinese Medicine Syndrome Elements and Symptoms

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Chinese Journal of Integrative Medicine 2012年第2期18卷 93-99页

作者：张磊于东林王义国张启明 Beijing Jiaotong University Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical Sciences

To select the best interestingness measure appropriate for evaluating the correlation between Chinese medicine （CM） 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 data set in this study had 44 600 records. Cold and heat were selected as the experimental CM syndrome elements. Three indicators calculated according to the distances between two CM syndrome elements were obtained in the experiment and 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 The method introduced in this paper may be used in other similar territories.

关键词： interestingness measure syndrome element symptom Chinese medicine

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Customer Activity Sequence Classification for Debt Prevention in Social Security

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Journal of Computer Science & Technology 2009年第6期24卷 1000-1009页

作者：张淮风 Yanchang Zhao 操龙兵张成奇 Hans Bohlscheid Payment Reviews Branch Business Integrity Division Centrelink Canberra Australia Centre for Quantum Computation and Intelligent Systems (QCIS) University of Technology Sydney Australia

From a data mining perspective, sequence classification is to build a classifier using frequent sequential patterns. However, mining for a complete set of sequential patterns on a large dataset can be extremely time-consuming and the large number of patterns discovered also makes the pattern selection and classifier building very time-consuming. The fact is that, in sequence classification, it is much more important to discover discriminative patterns than a complete pattern set. In this paper, we propose a novel hierarchical algorithm to build sequential classifiers using discriminative sequential patterns. Firstly, we mine for the sequential patterns which axe the most strongly correlated to each target class. In this step, an aggressive strategy is employed to select a small set of sequential patterns. Secondly, pattern pruning and serial coverage test are done on the mined patterns. The patterns that pass the serial test are used to build the sub-classifier at the first level of the final classifier. And thirdly, the training samples that cannot be covered are fed back to the sequential pattern mining stage with updated parameters. This process continues until predefined interestingness measure thresholds are reached, or all samples axe covered. The patterns generated in each loop form the sub-classifier at each level of the final classifier. Within this framework, the searching space can be reduced dramatically while a good classification performance is achieved. The proposed algorithm is tested in a real-world business application for debt prevention in social security area. The novel sequence classification algorithm shows the effectiveness and efficiency for predicting debt occurrences based on customer activity sequence data.

关键词： sequential pattern mining sequence classification coverage test interestingness measure

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维普期刊数据库

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关联规则的有趣性研究

关联规则的有趣性研究

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第二十一届中国数据库学术会议

作者：方艳别荣芳北京师范大学信息科学学院

关联规则挖掘用于发现大量数据中项集之间有趣的关联或相关联系,是数据挖掘中一个很重要的研究领域,最近几年已被业界所广泛研究。自1993年Agrawal R.提出此概念后,已出现了许多有效的挖掘算法。这些算法大都集中于如何提高算法的效

关键词： Data mining Association rule interestingness measure All-confidence Bond

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