Objective:To analyze the component law of Chinese patent medicines for anti-influenza and develop new prescriptions for anti-influenza by unsupervised data mining methods. Methods: Chinese patent medicine recipes for ...Objective:To analyze the component law of Chinese patent medicines for anti-influenza and develop new prescriptions for anti-influenza by unsupervised data mining methods. Methods: Chinese patent medicine recipes for anti-influenza were collected and recorded in the database, and then the correlation coefficient between herbs, core combinations of herbs and new prescriptions were analyzed by using modified mutual information, complex system entropy cluster and unsupervised hierarchical clustering, respectively. Results: Based on analysis of 126 Chinese patent medicine recipes, the frequency of each herb occurrence in these recipes, 54 frequently-used herb pairs, 34 core combinations were determined, and 4 new recipes for influenza were developed. Conclusion: Unsupervised data mining methods are able to mine the component law quickly and develop new prescriptions.展开更多
With the advent of the era of big data,buildings have become not only energy-intensive but also data-intensive.Data mining technologies have been widely utilized to release the values of massive amounts of building op...With the advent of the era of big data,buildings have become not only energy-intensive but also data-intensive.Data mining technologies have been widely utilized to release the values of massive amounts of building operation data with an aim of improving the operation performance of building energy systems.This paper aims at making a comprehensive literature review of the applications of data mining technologies in this domain.In general,data mining technologies can be classified into two categories,i.e.,supervised data mining technologies and unsupervised data mining technologies.In this field,supervised data mining technologies are usually utilized for building energy load prediction and fault detection/diagnosis.And unsupervised data mining technologies are usually utilized for building operation pattern identification and fault detection/diagnosis.Comprehensive discussions are made about the strengths and shortcomings of the data mining-based methods.Based on this review,suggestions for future researches are proposed towards effective and efficient data mining solutions for building energy systems.展开更多
基金supported by Scientific Research Special Project of TCM Profession (200907001E)Science and Technology Special Major Project for "Significant New Drugs Formulation" (2009ZX09301-005-02)
文摘Objective:To analyze the component law of Chinese patent medicines for anti-influenza and develop new prescriptions for anti-influenza by unsupervised data mining methods. Methods: Chinese patent medicine recipes for anti-influenza were collected and recorded in the database, and then the correlation coefficient between herbs, core combinations of herbs and new prescriptions were analyzed by using modified mutual information, complex system entropy cluster and unsupervised hierarchical clustering, respectively. Results: Based on analysis of 126 Chinese patent medicine recipes, the frequency of each herb occurrence in these recipes, 54 frequently-used herb pairs, 34 core combinations were determined, and 4 new recipes for influenza were developed. Conclusion: Unsupervised data mining methods are able to mine the component law quickly and develop new prescriptions.
基金This study is supported by the National Natural Science Foundation of China(Grant No.51706197).
文摘With the advent of the era of big data,buildings have become not only energy-intensive but also data-intensive.Data mining technologies have been widely utilized to release the values of massive amounts of building operation data with an aim of improving the operation performance of building energy systems.This paper aims at making a comprehensive literature review of the applications of data mining technologies in this domain.In general,data mining technologies can be classified into two categories,i.e.,supervised data mining technologies and unsupervised data mining technologies.In this field,supervised data mining technologies are usually utilized for building energy load prediction and fault detection/diagnosis.And unsupervised data mining technologies are usually utilized for building operation pattern identification and fault detection/diagnosis.Comprehensive discussions are made about the strengths and shortcomings of the data mining-based methods.Based on this review,suggestions for future researches are proposed towards effective and efficient data mining solutions for building energy systems.
