Pub Date : 2009-09-22DOI: 10.1109/GRC.2009.5255071
Zhongzhi Shi, Zuqiang Meng, Yuan Lu
At present GrC mainly is divided into three categories: computing with words (CW), rough set (RS) and quotient space (QS). From the perspective of essential characteristic of GrC, this demarcation is not comprehensive and accurate. In fact, CW is based on fuzzy granules (fuzzy subset) and both RS and QS are based on disjoint granules, which essentially are equivalence classes, either equal to each other or with empty overlap. In practical application, intersecting granules however need to be handled. Therefore there is another kind of GrC which is based on intersecting granules. This kind of GrC is referred to as tolerance GrC (TGrC) in our work. By constructing a Boolean algebra on super-granular space and a decision algebraic system, this paper will present an incomplete information system-based TGrC. With the TGrC, an example about extracting rules incomplete information is given, so as to show its basic principle.
{"title":"Tolerance Granular Computing based on incomplete information system","authors":"Zhongzhi Shi, Zuqiang Meng, Yuan Lu","doi":"10.1109/GRC.2009.5255071","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255071","url":null,"abstract":"At present GrC mainly is divided into three categories: computing with words (CW), rough set (RS) and quotient space (QS). From the perspective of essential characteristic of GrC, this demarcation is not comprehensive and accurate. In fact, CW is based on fuzzy granules (fuzzy subset) and both RS and QS are based on disjoint granules, which essentially are equivalence classes, either equal to each other or with empty overlap. In practical application, intersecting granules however need to be handled. Therefore there is another kind of GrC which is based on intersecting granules. This kind of GrC is referred to as tolerance GrC (TGrC) in our work. By constructing a Boolean algebra on super-granular space and a decision algebraic system, this paper will present an incomplete information system-based TGrC. With the TGrC, an example about extracting rules incomplete information is given, so as to show its basic principle.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129590349","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255110
Song Jin, Hongfei Lin, Sui Su
In traditional query expansion techniques, we choose the expansion terms based on their weights in the relevant documents. However, this kind of approaches does not take into account the semantic relationship between the original query terms and the expansion terms. Folksonomy is a social service in Web 2.0, which provides a large amount of social annotations. As the core of folksonomy, tags are high quality descriptors of the information contents and topics. Moreover, different tags describing the same information resource are semantically related to some extent. In this paper, we propose a query expansion method that utilizes the tag co-occurrence information to select the most appropriate expansion terms. Experimental results show that our tag co-occurrence-based query expansion technique consistently improves retrieval performance, compared with no-expansion method. This means the expansion terms we selected are semantically related to the original query, and tags of folksonomy will be the new resource of expansion terms.
{"title":"Query expansion based on folksonomy tag co-occurrence analysis","authors":"Song Jin, Hongfei Lin, Sui Su","doi":"10.1109/GRC.2009.5255110","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255110","url":null,"abstract":"In traditional query expansion techniques, we choose the expansion terms based on their weights in the relevant documents. However, this kind of approaches does not take into account the semantic relationship between the original query terms and the expansion terms. Folksonomy is a social service in Web 2.0, which provides a large amount of social annotations. As the core of folksonomy, tags are high quality descriptors of the information contents and topics. Moreover, different tags describing the same information resource are semantically related to some extent. In this paper, we propose a query expansion method that utilizes the tag co-occurrence information to select the most appropriate expansion terms. Experimental results show that our tag co-occurrence-based query expansion technique consistently improves retrieval performance, compared with no-expansion method. This means the expansion terms we selected are semantically related to the original query, and tags of folksonomy will be the new resource of expansion terms.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129431925","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255046
Bizhou Xiong, Benting Wan
Nondedicated distributed system is composed of many computers. Every computer has its owner user who has the highest priority using the computer. Special attention must be paid to the utilizing ratio of computer resource while computing tasks are allocated to distributed system. A relative queue model is constructed in this paper which is used to process data with synchronous relationship among them and perform data collection in multicomputer distributed system so we can calculate not only the resource utilizing ratio of individual computer but also the resource utilizing ratio of the whole system. Consequently, it can perform dynamitic and real-time monitoring on distributed system resource utilizing ratio properly and conveniently. After implementing the model in a distributed system and comparing the implementation results with performance monitor in Window 2000, which is task manager, the results indicate that relative queue model proposed in this paper can real-timely and dynamically monitor multi-computer distributed system performance satisfactorily.
{"title":"Relative queue-based distributed system performance real-time dynamic monitor","authors":"Bizhou Xiong, Benting Wan","doi":"10.1109/GRC.2009.5255046","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255046","url":null,"abstract":"Nondedicated distributed system is composed of many computers. Every computer has its owner user who has the highest priority using the computer. Special attention must be paid to the utilizing ratio of computer resource while computing tasks are allocated to distributed system. A relative queue model is constructed in this paper which is used to process data with synchronous relationship among them and perform data collection in multicomputer distributed system so we can calculate not only the resource utilizing ratio of individual computer but also the resource utilizing ratio of the whole system. Consequently, it can perform dynamitic and real-time monitoring on distributed system resource utilizing ratio properly and conveniently. After implementing the model in a distributed system and comparing the implementation results with performance monitor in Window 2000, which is task manager, the results indicate that relative queue model proposed in this paper can real-timely and dynamically monitor multi-computer distributed system performance satisfactorily.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129580531","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255034
Xibei Yang, Jing-yu Yang, Xiaohua Hu
Rough set models based on the tolerance and similarity relations, are constructed to deal with incomplete information systems. Unfortunately, tolerance and similarity relations have their own limitations because the former is too loose while the latter is too strict in classification analysis. To make a reasonable and flexible classification in incomplete information system, a new binary relation is proposed in this paper. This new binary relation is only reflective and it is a generalization of tolerance and similarity relations. Furthermore, three different rough set models based on the above three different binary relations are compared and then some important properties are obtained. Finally, the direct approach to certain and possible rules induction in incomplete information system is investigated, an illustrative example is analyzed to substantiate the conceptual arguments.
{"title":"A new rough set model for knowledge acquisition in incomplete information system","authors":"Xibei Yang, Jing-yu Yang, Xiaohua Hu","doi":"10.1109/GRC.2009.5255034","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255034","url":null,"abstract":"Rough set models based on the tolerance and similarity relations, are constructed to deal with incomplete information systems. Unfortunately, tolerance and similarity relations have their own limitations because the former is too loose while the latter is too strict in classification analysis. To make a reasonable and flexible classification in incomplete information system, a new binary relation is proposed in this paper. This new binary relation is only reflective and it is a generalization of tolerance and similarity relations. Furthermore, three different rough set models based on the above three different binary relations are compared and then some important properties are obtained. Finally, the direct approach to certain and possible rules induction in incomplete information system is investigated, an illustrative example is analyzed to substantiate the conceptual arguments.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128256698","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255002
T. Lin
5th GrC model is the formal model specified into the category of sets It is a theory of ordered granules, namely, granules are ordered “subsets” of the universe, We extract a 5th GrC model from a set of web pages. A granule is a high frequent sequence of keywords, It is a tuple in a relation and naturally carries some concept expressed in web pages. The concept analysis in this paper is about true human concepts that are expressed in web documents.
{"title":"Concept analysis in web informatics- 5th GrC model - Using ordered granules","authors":"T. Lin","doi":"10.1109/GRC.2009.5255002","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255002","url":null,"abstract":"5th GrC model is the formal model specified into the category of sets It is a theory of ordered granules, namely, granules are ordered “subsets” of the universe, We extract a 5th GrC model from a set of web pages. A granule is a high frequent sequence of keywords, It is a tuple in a relation and naturally carries some concept expressed in web pages. The concept analysis in this paper is about true human concepts that are expressed in web documents.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128641568","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255052
Ming-Fen Wu, Ting-Liang Wang
Calculating the core of a decision information system is the start of information reduction and a key step of decision rule making. In this paper, we analyze essential characters of core attributes of decision information system according to rough set theory. Then researching the relationship between discernibility matrix' single attribute element and a core attribute. As algorithms, which were given out by Skowron and Zhang, has highly computing complicacy for calculating the core of decision system based on discernibility matrix. This paper gives out an improved algorithm, and proves it to be right. The simulation experiments shows that the new algorithm's calculating work will be reduced according to the proportion of inconsistent objects has risen.
{"title":"Characterization and algorithm of decision system's core based discernibility matrix","authors":"Ming-Fen Wu, Ting-Liang Wang","doi":"10.1109/GRC.2009.5255052","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255052","url":null,"abstract":"Calculating the core of a decision information system is the start of information reduction and a key step of decision rule making. In this paper, we analyze essential characters of core attributes of decision information system according to rough set theory. Then researching the relationship between discernibility matrix' single attribute element and a core attribute. As algorithms, which were given out by Skowron and Zhang, has highly computing complicacy for calculating the core of decision system based on discernibility matrix. This paper gives out an improved algorithm, and proves it to be right. The simulation experiments shows that the new algorithm's calculating work will be reduced according to the proportion of inconsistent objects has risen.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130921541","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255148
Qingshan Deng, Guoping Mei
Auditing practices nowadays have to cope with an increasing number of fraudulent financial statements (FFS). Based on data mining techniques, researchers have made some studies and have found that the techniques can facilitate auditors in accomplishing the task of detection of FFS. However, most of the techniques used in the detection of FFS are supervised methods. Clustering, one kind of unsupervised data mining technique, has almost never been used. Therefore, considering the characteristics of FFS and self-organizing map(SOM), a model combining SOM and K-means clustering based on a clustering validity measure is designed. To carry out the experiment, 100 financial statements from Chinese listed companies during 1999–2006 are selected as experimental sample according to some specific standards. 47 financial ratios are chosen as variables. The model is applied to the data and good experimental results are obtained.
{"title":"Combining self-organizing map and K-means clustering for detecting fraudulent financial statements","authors":"Qingshan Deng, Guoping Mei","doi":"10.1109/GRC.2009.5255148","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255148","url":null,"abstract":"Auditing practices nowadays have to cope with an increasing number of fraudulent financial statements (FFS). Based on data mining techniques, researchers have made some studies and have found that the techniques can facilitate auditors in accomplishing the task of detection of FFS. However, most of the techniques used in the detection of FFS are supervised methods. Clustering, one kind of unsupervised data mining technique, has almost never been used. Therefore, considering the characteristics of FFS and self-organizing map(SOM), a model combining SOM and K-means clustering based on a clustering validity measure is designed. To carry out the experiment, 100 financial statements from Chinese listed companies during 1999–2006 are selected as experimental sample according to some specific standards. 47 financial ratios are chosen as variables. The model is applied to the data and good experimental results are obtained.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127858483","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255044
Xiaolin Xu, Guanglin Xu, Jia-li Feng
Based on input and output relationship of Qualitative Mapping(QM), the attribute computing network model has been created. It brings forward a kind of computing method using input to adjust qualitative benchmark of attribute network, which makes it possible to achieve pattern recognition. Now the new attribute computing network model combined pattern recognition with synthetic evaluation is established. Firstly qualitative benchmarks of indexes are gotten by boundary study, and then by way of marking, preference for indexes is obtained, and lastly a set of satisfactory degrees for indexes is computed and outputted in descending sequence which ameliorates the effect of old satisfactory degree. Finally the simulation experiment is carried out to validate the theoretical model.
{"title":"A kind of synthetic evaluation method based on the attribute computing network","authors":"Xiaolin Xu, Guanglin Xu, Jia-li Feng","doi":"10.1109/GRC.2009.5255044","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255044","url":null,"abstract":"Based on input and output relationship of Qualitative Mapping(QM), the attribute computing network model has been created. It brings forward a kind of computing method using input to adjust qualitative benchmark of attribute network, which makes it possible to achieve pattern recognition. Now the new attribute computing network model combined pattern recognition with synthetic evaluation is established. Firstly qualitative benchmarks of indexes are gotten by boundary study, and then by way of marking, preference for indexes is obtained, and lastly a set of satisfactory degrees for indexes is computed and outputted in descending sequence which ameliorates the effect of old satisfactory degree. Finally the simulation experiment is carried out to validate the theoretical model.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133716585","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255133
Bo Guo, Wei Chen, Zhenyuan Wang
In some real optimization problems, the objective function may not be differentiable with respect to the unknown parameters at some points such that the gradient does not exist at those points. Replacing the classical gradient, this paper tries to use pseudo gradient search for solving a nonlinear optimization problem—nonlinear multiregression based on the Choquet integral with a linear core. It is a local search method with rapid search speed.
{"title":"Pseudo gradient search for solving nonlinear multiregression based on the Choquet integral","authors":"Bo Guo, Wei Chen, Zhenyuan Wang","doi":"10.1109/GRC.2009.5255133","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255133","url":null,"abstract":"In some real optimization problems, the objective function may not be differentiable with respect to the unknown parameters at some points such that the gradient does not exist at those points. Replacing the classical gradient, this paper tries to use pseudo gradient search for solving a nonlinear optimization problem—nonlinear multiregression based on the Choquet integral with a linear core. It is a local search method with rapid search speed.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116612519","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 : 2009-09-22DOI: 10.1109/GRC.2009.5255066
Jieli Sun, Zhiqing Zhu, Y. Wang
This paper studies Personalized recommendation adaptive dynamic case expression according to the characteristics of the method of case-based reasoning (CBR) and the personal recommendation cases. And the design thought of the personalized recommendation adaptive dynamic case expression are discussed. Finally, the design methods of the personalized recommendation adaptive dynamic case expression are analyzed.
{"title":"Research on Personalized recommendation adaptive dynamic case expression","authors":"Jieli Sun, Zhiqing Zhu, Y. Wang","doi":"10.1109/GRC.2009.5255066","DOIUrl":"https://doi.org/10.1109/GRC.2009.5255066","url":null,"abstract":"This paper studies Personalized recommendation adaptive dynamic case expression according to the characteristics of the method of case-based reasoning (CBR) and the personal recommendation cases. And the design thought of the personalized recommendation adaptive dynamic case expression are discussed. Finally, the design methods of the personalized recommendation adaptive dynamic case expression are analyzed.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128410816","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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