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An Improved Algorithm for Mining correlation Item Pairs

Computers, Materials & Continua 2020年第10期65卷 337-354页

作者： Tao Li Yongzhen Ren Yongjun Ren Jinyue Xia College of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China College of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China International Business Machines Corporation(IBM) New YorkUSA

Apriori algorithm is often used in traditional association rules mining,searching for the mode of higher frequency.Then the correlation rules are obtained by detected the correlation of the item sets,but this tends to ignore low-support high-correlation of association rules.In view of the above problems,some scholars put forward the positive correlation coefficient based on Phi correlation to avoid the embarrassment caused by Apriori algorithm.It can dig item sets with low-support but high-correlation.Although the algorithm has pruned the search space,it is not obvious that the performance of the running time based on the big data set is reduced,and the correlation pairs can be meaningless.This paper presents an improved mining algorithm with new association rules based on interestingness for correlation pairs,using an upper bound on interestingness of the supersets to prune the search space.It greatly reduces the running time,and filters the meaningless correlation pairs according to the constraints of the redundancy.Compared with the algorithm based on the Phi correlation coefficient,the new algorithm has been significantly improved in reducing the running time,the result has pruned the redundant correlation pairs.So it improves the mining efficiency and accuracy.

关键词： Interestingness item pairs positive correlation association rules redundancy

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Analysis of the Microphysical Structure of Heavy Fog Using a Droplet Spectrometer:A Case Study

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Advances in Atmospheric Sciences 2010年第6期27卷 1259-1275页

作者：牛生杰陆春松刘延刚赵丽娟吕晶晶杨军 Key Laboratory of Meteorological Disaster of Ministry of Education School of Atmospheric PhysicsNanjing University of Information Science & Technology Brookhaven National Laboratory Atmospheric Sciences Division

The microphysical properties of a long-lasting heavy fog event are examined based on the results from a comprehensive field campaign conducted during the winter of 2006 at Pancheng （32.2°N, 118.7°E）, Jiangsu Province, China. It is demonstrated that the key microphysical properties （liquid water content, fog droplet concentration, mean radius and standard deviation） exhibited positive correlations with one another in general, and that the 5-min-average maximum value of fog liquid water content was sometimes greater than 0.5 g m-3. Further analysis shows that the unique combination of positive correlations likely arose from the simultaneous supply of moist air and fog condensation nuclei associated with the advection of warm air, which further led to high liquid water content. High values of liquid water content and droplet concentration conspired to cause low visibility （〈50 m） for a prolonged period of about 40 h. Examination of the microphysical relationships conditioned by the corresponding autoconversion threshold functions shows that the collision-coalescence process was sometimes likely to occur, weakening the positive correlations induced by droplet activation and condensational growth. Statistical analysis shows that the observed droplet size distribution can be described well by the Gamma distribution.

关键词： fog microphysics positive correlation high liquid water content low visibility warm and moistair

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Collaborative Filtering Algorithms Based on Kendall correlation in Recommender Systems

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Wuhan University Journal of Natural Sciences 2006年第5期11卷 1086-1090页

作者： YAO Yu ZHU Shanfeng CHEN Xinmeng School of Computer Wuhan University Wuhan430072 Hubei China Institute for Chemical Research Kyoto UniversityKyoto 611-0011 Japan

In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.

关键词： Kendall correlation collaborative filtering algorithms recommender systems positive correlation

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Order-preservation and positive correlations for Diffusion Processes on Torus

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Northeastern Mathematical Journal 1994年第2期10卷 149-158页

作者：王凤雨严士健

This is a sequel to the papers [1] and [2] in which the criteria on the order-preservation and positive cortelations for multidimensional diffusion processes were presented. This paper proves the aimilar criterin fo... 详细信息

关键词： Order-preservation positive correlation Monotonicity Diffusion Process Torus

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