An information system is a type of knowledge representation,and attribute reduction is crucial in big data,machine learning,data mining,and intelligent systems.There are several ways for solving attribute reduction pr...An information system is a type of knowledge representation,and attribute reduction is crucial in big data,machine learning,data mining,and intelligent systems.There are several ways for solving attribute reduction problems,but they all require a common categorization.The selection of features in most scientific studies is a challenge for the researcher.When working with huge datasets,selecting all available attributes is not an option because it frequently complicates the study and decreases performance.On the other side,neglecting some attributes might jeopardize data accuracy.In this case,rough set theory provides a useful approach for identifying superfluous attributes that may be ignored without sacrificing any significant information;nonetheless,investigating all available combinations of attributes will result in some problems.Furthermore,because attribute reduction is primarily a mathematical issue,technical progress in reduction is dependent on the advancement of mathematical models.Because the focus of this study is on the mathematical side of attribute reduction,we propose some methods to make a reduction for information systems according to classical rough set theory,the strength of rules and similarity matrix,we applied our proposed methods to several examples and calculate the reduction for each case.These methods expand the options of attribute reductions for researchers.展开更多
A web page clustering algorithm called PageCluster and the improved algorithm ImPageCluster solving overlapping are proposed. These methods not only take the web structure and page hyperlink into account, but also con...A web page clustering algorithm called PageCluster and the improved algorithm ImPageCluster solving overlapping are proposed. These methods not only take the web structure and page hyperlink into account, but also consider the importance of each page which is described as in-weight and out-weight. Compared with the traditional clustering methods, the experiments show that the runtimes of the proposed algorithms are less with the improved accuracies.展开更多
K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper propo...K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved algorithm can effectively avoid the random selection of initial center points, therefore it can provide effective initial points for clustering process, and reduce the fluctuation of clustering results which are resulted from initial points selections, thus a better clustering quality can be obtained. The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable.展开更多
目的通过对以神经科学集群建设为特色的三级甲等医院的神经专科能力进行评估,为国内医院的特色专科建设提供参考。方法从服务能力、技术能力、质量安全和服务效率4个维度建立神经外科、神经内科专科能力评估指标体系,通过优劣解距离(tec...目的通过对以神经科学集群建设为特色的三级甲等医院的神经专科能力进行评估,为国内医院的特色专科建设提供参考。方法从服务能力、技术能力、质量安全和服务效率4个维度建立神经外科、神经内科专科能力评估指标体系,通过优劣解距离(technique for order preference by similarity to ideal solution,TOPSIS)法纵向比较首都医科大学附属北京天坛医院神经专科2019—2023年发展趋势,并采用波士顿矩阵深入分析神经专科各亚专业建设情况。结果2019—2023年首都医科大学附属北京天坛医院神经专科TOPSIS综合得分指数呈上升趋势。神经外科以胶质瘤诊治为主的肿瘤专业1,在技术能力和质量安全方面得分指数最高;神经内科以脑血管病为主的亚专业,在技术能力和服务效率方面得分指数较高,以上两个亚专业在波士顿矩阵中均处于优势巩固区。结论2019—2023年首都医科大学附属北京天坛医院神经专科诊治能力不断提升。神经外科亚专业中,专科能力排名最高的为肿瘤专业1,诊疗技术难度较高,同时医疗质量负性事件发生率低。神经内科各亚专业中,脑血管专业诊疗技术难度高且服务高效。展开更多
文摘An information system is a type of knowledge representation,and attribute reduction is crucial in big data,machine learning,data mining,and intelligent systems.There are several ways for solving attribute reduction problems,but they all require a common categorization.The selection of features in most scientific studies is a challenge for the researcher.When working with huge datasets,selecting all available attributes is not an option because it frequently complicates the study and decreases performance.On the other side,neglecting some attributes might jeopardize data accuracy.In this case,rough set theory provides a useful approach for identifying superfluous attributes that may be ignored without sacrificing any significant information;nonetheless,investigating all available combinations of attributes will result in some problems.Furthermore,because attribute reduction is primarily a mathematical issue,technical progress in reduction is dependent on the advancement of mathematical models.Because the focus of this study is on the mathematical side of attribute reduction,we propose some methods to make a reduction for information systems according to classical rough set theory,the strength of rules and similarity matrix,we applied our proposed methods to several examples and calculate the reduction for each case.These methods expand the options of attribute reductions for researchers.
基金Sponsored bythe Huo Ying-Dong Education Foundation of China(91101)
文摘A web page clustering algorithm called PageCluster and the improved algorithm ImPageCluster solving overlapping are proposed. These methods not only take the web structure and page hyperlink into account, but also consider the importance of each page which is described as in-weight and out-weight. Compared with the traditional clustering methods, the experiments show that the runtimes of the proposed algorithms are less with the improved accuracies.
文摘K-means algorithm is one of the most widely used algorithms in the clustering analysis. To deal with the problem caused by the random selection of initial center points in the traditional al- gorithm, this paper proposes an improved K-means algorithm based on the similarity matrix. The im- proved algorithm can effectively avoid the random selection of initial center points, therefore it can provide effective initial points for clustering process, and reduce the fluctuation of clustering results which are resulted from initial points selections, thus a better clustering quality can be obtained. The experimental results also show that the F-measure of the improved K-means algorithm has been greatly improved and the clustering results are more stable.
文摘目的通过对以神经科学集群建设为特色的三级甲等医院的神经专科能力进行评估,为国内医院的特色专科建设提供参考。方法从服务能力、技术能力、质量安全和服务效率4个维度建立神经外科、神经内科专科能力评估指标体系,通过优劣解距离(technique for order preference by similarity to ideal solution,TOPSIS)法纵向比较首都医科大学附属北京天坛医院神经专科2019—2023年发展趋势,并采用波士顿矩阵深入分析神经专科各亚专业建设情况。结果2019—2023年首都医科大学附属北京天坛医院神经专科TOPSIS综合得分指数呈上升趋势。神经外科以胶质瘤诊治为主的肿瘤专业1,在技术能力和质量安全方面得分指数最高;神经内科以脑血管病为主的亚专业,在技术能力和服务效率方面得分指数较高,以上两个亚专业在波士顿矩阵中均处于优势巩固区。结论2019—2023年首都医科大学附属北京天坛医院神经专科诊治能力不断提升。神经外科亚专业中,专科能力排名最高的为肿瘤专业1,诊疗技术难度较高,同时医疗质量负性事件发生率低。神经内科各亚专业中,脑血管专业诊疗技术难度高且服务高效。
