Pub Date : 1900-01-01DOI: 10.1109/GrC.2012.6468644
Lunwen Wang, Lin Zhang
{"title":"Incomplete data mining based on fuzzy tolerance quotient space","authors":"Lunwen Wang, Lin Zhang","doi":"10.1109/GrC.2012.6468644","DOIUrl":"https://doi.org/10.1109/GrC.2012.6468644","url":null,"abstract":"","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123805155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/GrC.2013.6740449
Jianmin Zhang, X. Duan, Li Lin, Yuhan Ma
{"title":"A study on the relationship between rural-urban income gap and human capital investment disparity in China: A case study on Yunnan province","authors":"Jianmin Zhang, X. Duan, Li Lin, Yuhan Ma","doi":"10.1109/GrC.2013.6740449","DOIUrl":"https://doi.org/10.1109/GrC.2013.6740449","url":null,"abstract":"","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126279615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/GrC.2012.6468654
Jinping Li, Qin Min, Yingjie Xia, Yanbin Han
{"title":"Remarks on a novel statistical histogram - Average Scene Cumulative Histogram","authors":"Jinping Li, Qin Min, Yingjie Xia, Yanbin Han","doi":"10.1109/GrC.2012.6468654","DOIUrl":"https://doi.org/10.1109/GrC.2012.6468654","url":null,"abstract":"","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116442947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/GRC.2011.6122597
Shujuan Gu, Sen Wu
Lazy learning has shown promising reliability in data stream classification mining, which suffer from ‘Curse of dimensionality’ in broad applications. Conventional Attribute selection methods always seek promising subspace by ranking all the attributes, which is not suitable for lazy learning, and suffer from high computing complexity. We proposed a novel attributes selection method ‘DistinGuishing Subspace (DG-Subspace)’, which lay high values on the performance of attributes as a group instead of single attribute with higher ranks. ‘DistinGuishing Pattern Tree (DGP-tree)’ was formed to compress dataset, based on which a heuristic method to seek DG-subspace was raised, with linear scalability. Theoretic analysis and numeric experiment justified the effectiveness and efficiency of the method.
{"title":"DG-subspace: A novel attributes selection method for lazy learning","authors":"Shujuan Gu, Sen Wu","doi":"10.1109/GRC.2011.6122597","DOIUrl":"https://doi.org/10.1109/GRC.2011.6122597","url":null,"abstract":"Lazy learning has shown promising reliability in data stream classification mining, which suffer from ‘Curse of dimensionality’ in broad applications. Conventional Attribute selection methods always seek promising subspace by ranking all the attributes, which is not suitable for lazy learning, and suffer from high computing complexity. We proposed a novel attributes selection method ‘DistinGuishing Subspace (DG-Subspace)’, which lay high values on the performance of attributes as a group instead of single attribute with higher ranks. ‘DistinGuishing Pattern Tree (DGP-tree)’ was formed to compress dataset, based on which a heuristic method to seek DG-subspace was raised, with linear scalability. Theoretic analysis and numeric experiment justified the effectiveness and efficiency of the method.","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122834952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Study of Normal Form of Relational Database Based on Rough Sets Theory","authors":"Qiusheng An, Gaoping Wang, Wenxiu Zhang","doi":"10.1109/GrC.2007.32","DOIUrl":"https://doi.org/10.1109/GrC.2007.32","url":null,"abstract":"","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121516017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A new theory of complexity science management - Big organization","authors":"Zhan Zheng, Wei Zhao, Xiaodi Zhang, Xuegong Zeng, Xiaojing Zheng","doi":"10.1109/GrC.2013.6740453","DOIUrl":"https://doi.org/10.1109/GrC.2013.6740453","url":null,"abstract":"","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121750552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Qing Lv, Xiaoming Han, Gang Xie, Gaowei Yan, Jun Xie
—A regression method of Support Vector Machines in the case of a large number of sample data. Hierarchies of various granularities for the data set are constructed by density clustering algorithm. In coarse-granularity level, abnormal sample data are excluded, while part of dense repeated samples are removed in fine-granularity level. After pretreating the sample set by the method mentioned above, Support Vector Regression is trained to construct a regression model. In this paper, the prediction model of coke mechanical strength is established by the means. The result indicates that Support Vector Regression Algorithm based on granularity has low computational complexity and high speed, moreover eliminating noise sample data and removing the dense samples do not affect the distribution and prediction effect of the original sample set. It is an effective measure of regression with the large sample data.
{"title":"Research of Support Vector Regression Algorithm Based on Granularity","authors":"Qing Lv, Xiaoming Han, Gang Xie, Gaowei Yan, Jun Xie","doi":"10.1109/GrC.2010.17","DOIUrl":"https://doi.org/10.1109/GrC.2010.17","url":null,"abstract":"—A regression method of Support Vector Machines in the case of a large number of sample data. Hierarchies of various granularities for the data set are constructed by density clustering algorithm. In coarse-granularity level, abnormal sample data are excluded, while part of dense repeated samples are removed in fine-granularity level. After pretreating the sample set by the method mentioned above, Support Vector Regression is trained to construct a regression model. In this paper, the prediction model of coke mechanical strength is established by the means. The result indicates that Support Vector Regression Algorithm based on granularity has low computational complexity and high speed, moreover eliminating noise sample data and removing the dense samples do not affect the distribution and prediction effect of the original sample set. It is an effective measure of regression with the large sample data.","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125135793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/GRC.2011.6122689
Xiaohui Wang, Kehe Wu, Yuhan Xu
This paper analyzed the application characteristics of the GIS in the power industry, and treated the data model and structure of the power GIS. It proposed a conceptual data model “four-dimension, four-division” based on the knowledge base, and proposed a cube structure based on the overall object-oriented power GIS model. The cube structure separate managed the semantic features, geometry, topology, real-time monitoring and other information of the electrical facilities in the form of components, and formed a highly efficient power GIS spatial data model.
{"title":"The data model and structure of power GIS","authors":"Xiaohui Wang, Kehe Wu, Yuhan Xu","doi":"10.1109/GRC.2011.6122689","DOIUrl":"https://doi.org/10.1109/GRC.2011.6122689","url":null,"abstract":"This paper analyzed the application characteristics of the GIS in the power industry, and treated the data model and structure of the power GIS. It proposed a conceptual data model “four-dimension, four-division” based on the knowledge base, and proposed a cube structure based on the overall object-oriented power GIS model. The cube structure separate managed the semantic features, geometry, topology, real-time monitoring and other information of the electrical facilities in the form of components, and formed a highly efficient power GIS spatial data model.","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124582761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/GRC.2011.6122639
Zuqiang Meng, Zhongzhi Shi, Ke Xu
This paper systematically studies the problem of decision rule acquisition in inconsistent incomplete decision systems (IIDSs). First, a tolerance granular framework model based on tolerance granular computing is presented, which is suitable for variety types of decision rules in IIDSs; secondly, with the proposed model, a framework for acquiring all minimum decision rule sets for each type is given, which solves the problem of decision rule acquisition in IIDSs to a certain degree; finally, an example is given to show the efficiency of our framework.
{"title":"A general framework for rule acquisition based on tolerance granular computing in IIDSs","authors":"Zuqiang Meng, Zhongzhi Shi, Ke Xu","doi":"10.1109/GRC.2011.6122639","DOIUrl":"https://doi.org/10.1109/GRC.2011.6122639","url":null,"abstract":"This paper systematically studies the problem of decision rule acquisition in inconsistent incomplete decision systems (IIDSs). First, a tolerance granular framework model based on tolerance granular computing is presented, which is suitable for variety types of decision rules in IIDSs; secondly, with the proposed model, a framework for acquiring all minimum decision rule sets for each type is given, which solves the problem of decision rule acquisition in IIDSs to a certain degree; finally, an example is given to show the efficiency of our framework.","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121100436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/GRC.2005.1547308
Yanqin Zhu, Fanzhang Li, Yuemei Hu
A dynamic fuzzy expert database system is a combination of a dynamic fuzzy database and the expert system. This paper proposes the methods of designing a DF expert database system. Then we design a teacher evaluation DF expert database system by using the techniques. Practice shows that the application of dynamic fuzzy database in the expert system not only extends functions of the normal database system, but also resolves dynamic fuzzy problems efficiently.
{"title":"The design and application of dynamic fuzzy expert database system","authors":"Yanqin Zhu, Fanzhang Li, Yuemei Hu","doi":"10.1109/GRC.2005.1547308","DOIUrl":"https://doi.org/10.1109/GRC.2005.1547308","url":null,"abstract":"A dynamic fuzzy expert database system is a combination of a dynamic fuzzy database and the expert system. This paper proposes the methods of designing a DF expert database system. Then we design a teacher evaluation DF expert database system by using the techniques. Practice shows that the application of dynamic fuzzy database in the expert system not only extends functions of the normal database system, but also resolves dynamic fuzzy problems efficiently.","PeriodicalId":126161,"journal":{"name":"IEEE International Conference on Granular Computing","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122505987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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