Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters.A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets...Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters.A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets.This paper focuses on cluster analysis based on neutrosophic set implication,i.e.,a k-means algorithm with a threshold-based clustering technique.This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm.To evaluate the validity of the proposed method,several validity measures and validity indices are applied to the Iris dataset(from the University of California,Irvine,Machine Learning Repository)along with k-means and threshold-based clustering algorithms.The proposed method results in more segregated datasets with compacted clusters,thus achieving higher validity indices.The method also eliminates the limitations of threshold-based clustering algorithm and validates measures and respective indices along with k-means and threshold-based clustering algorithms.展开更多
This paper presents a new idea, named as modeling multisensor-heterogeneous information, to incorporate the fuzzy logic methodologies with mulitsensor-multitarget system under the framework of random set theory. First...This paper presents a new idea, named as modeling multisensor-heterogeneous information, to incorporate the fuzzy logic methodologies with mulitsensor-multitarget system under the framework of random set theory. Firstly, based on strong random set and weak random set, the unified form to describe both data (unambiguous information) and fuzzy evidence (uncertain information) is introduced. Secondly, according to signatures of fuzzy evidence, two Bayesian-markov nonlinear measurement models are proposed to fuse effectively data and fuzzy evidence. Thirdly, by use of "the models-based signature-matching scheme", the operation of the statistics of fuzzy evidence defined as random set can be translated into that of the membership functions of relative point state variables. These works are the basis to construct qualitative measurement models and to fuse data and fuzzy evidence.展开更多
数据在传递过程中,经常出现两类现象:一些被传递的数据在传递中发生部分数据元丢失;一些未知的数据元入侵到被传递的数据内。这两类现象使得被传递的数据出现"异常"。利用一个新的数学模型,给出两类现象的理论研究与应用。这...数据在传递过程中,经常出现两类现象:一些被传递的数据在传递中发生部分数据元丢失;一些未知的数据元入侵到被传递的数据内。这两类现象使得被传递的数据出现"异常"。利用一个新的数学模型,给出两类现象的理论研究与应用。这个新的数学模型是P-集合(packet sets),P-集合是由内P-集合XF珚(internal packet set XF珚)与外P-集合XF(outer packet set XF)构成的集合对;或者,(XF珚,XF)是P-集合。给出数据的F珚-依赖、F-依赖的概念与特性,提出数据的依赖定理,给出异常数据被分离的应用。数据依赖是P-集合诸多应用特性之一。P-集合是研究动态数据系统的一个新理论与新方法。展开更多
The plausibility relation which is generalization of fuzzy relation and probabilistic relation is proposed in thepaper. We think data mining to be a process of finding the plausibility relation in database and correla...The plausibility relation which is generalization of fuzzy relation and probabilistic relation is proposed in thepaper. We think data mining to be a process of finding the plausibility relation in database and correlativity measure tobe a particular plausibility relation based on correlativity sets. The critical calculates such as the accuracy of the roughsets, the confidence and the bayesian form in data mining can be united using the correlativity measure. The GPDM(General Process of Data Mining)that represents the nature of data mining is also proposed. The data mining theoreti-cal foundation and frameworks based on correlativity sets are also given and discussed in the paper.展开更多
文摘Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters.A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets.This paper focuses on cluster analysis based on neutrosophic set implication,i.e.,a k-means algorithm with a threshold-based clustering technique.This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm.To evaluate the validity of the proposed method,several validity measures and validity indices are applied to the Iris dataset(from the University of California,Irvine,Machine Learning Repository)along with k-means and threshold-based clustering algorithms.The proposed method results in more segregated datasets with compacted clusters,thus achieving higher validity indices.The method also eliminates the limitations of threshold-based clustering algorithm and validates measures and respective indices along with k-means and threshold-based clustering algorithms.
基金Supported by the NSFC(No.60434020,60572051)Science and Technology Key Item of Ministry of Education of the PRC( No.205-092)the ZJNSF(No. R106745)
文摘This paper presents a new idea, named as modeling multisensor-heterogeneous information, to incorporate the fuzzy logic methodologies with mulitsensor-multitarget system under the framework of random set theory. Firstly, based on strong random set and weak random set, the unified form to describe both data (unambiguous information) and fuzzy evidence (uncertain information) is introduced. Secondly, according to signatures of fuzzy evidence, two Bayesian-markov nonlinear measurement models are proposed to fuse effectively data and fuzzy evidence. Thirdly, by use of "the models-based signature-matching scheme", the operation of the statistics of fuzzy evidence defined as random set can be translated into that of the membership functions of relative point state variables. These works are the basis to construct qualitative measurement models and to fuse data and fuzzy evidence.
文摘数据在传递过程中,经常出现两类现象:一些被传递的数据在传递中发生部分数据元丢失;一些未知的数据元入侵到被传递的数据内。这两类现象使得被传递的数据出现"异常"。利用一个新的数学模型,给出两类现象的理论研究与应用。这个新的数学模型是P-集合(packet sets),P-集合是由内P-集合XF珚(internal packet set XF珚)与外P-集合XF(outer packet set XF)构成的集合对;或者,(XF珚,XF)是P-集合。给出数据的F珚-依赖、F-依赖的概念与特性,提出数据的依赖定理,给出异常数据被分离的应用。数据依赖是P-集合诸多应用特性之一。P-集合是研究动态数据系统的一个新理论与新方法。
文摘The plausibility relation which is generalization of fuzzy relation and probabilistic relation is proposed in thepaper. We think data mining to be a process of finding the plausibility relation in database and correlativity measure tobe a particular plausibility relation based on correlativity sets. The critical calculates such as the accuracy of the roughsets, the confidence and the bayesian form in data mining can be united using the correlativity measure. The GPDM(General Process of Data Mining)that represents the nature of data mining is also proposed. The data mining theoreti-cal foundation and frameworks based on correlativity sets are also given and discussed in the paper.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.60573069)国家教育部科学技术研究重点项目计划(the Key Scientific and Technical Research Project of Ministry of Education of China under Grant No.20602)+1 种基金河北省自然科学基金(the Natural Science Foundation of Hebei Province under Grant No.F2004000129)河北省教育厅科研计划重点项目(the Key Scientific Research Project of Department of Education of Hebei Province of China under Grant No.2005011D)
