A vertex labeling f ： V →Z2 of a simple graph G = （V, E） induces two edge labelings The friendly index set and the product-cordial index set of G are defined as the setsf is friendly}. In this paper we study and d...A vertex labeling f ： V →Z2 of a simple graph G = （V, E） induces two edge labelings The friendly index set and the product-cordial index set of G are defined as the setsf is friendly}. In this paper we study and determine the connection between the friendly index sets and product-cordial index sets of 2-regular graphs and generalized wheel graphs.展开更多
Let G =（V, E） be a connected simple graph. A labeling f ： V → Z2 induces an edge labeling f＊ ： E → Z2 defined by f＊（xy） = f（x） ＋f（y） for each xy ∈ E. For i ∈ Z2, let vf（i） = ｜f^-1（i）｜ and ef（i...Let G =（V, E） be a connected simple graph. A labeling f ： V → Z2 induces an edge labeling f＊ ： E → Z2 defined by f＊（xy） = f（x） ＋f（y） for each xy ∈ E. For i ∈ Z2, let vf（i） = ｜f^-1（i）｜ and ef（i） = ｜f＊^-1（i）｜. A labeling f is called friendly if ｜vf（1） - vf（0）｜ ≤ 1. For a friendly labeling f of a graph G, we define the friendly index of G under f by if（G） = e（1） - el（0）. The set [if（G） ｜ f is a friendly labeling of G} is called the full friendly index set of G, denoted by FFI（G）. In this paper, we will determine the full friendly index set of every Cartesian product of two cycles.展开更多
In this paper, we introduce the concept of the general butterfly graph B[m,n;d] for integers m,n ≥ 3, d ≥ 1, determine its balance index set, and give the necessary and sufficient condition for balanced graph B[m,n;...In this paper, we introduce the concept of the general butterfly graph B[m,n;d] for integers m,n ≥ 3, d ≥ 1, determine its balance index set, and give the necessary and sufficient condition for balanced graph B[m,n;d] to exist.展开更多
Let G be a connected simple graph with vertex set V(G)and edge set E(G).A binary vertex labeling f:V(G)→Z2,is said to be friendly if the number of vertices with different labels differs by at most one.Each vertex fri...Let G be a connected simple graph with vertex set V(G)and edge set E(G).A binary vertex labeling f:V(G)→Z2,is said to be friendly if the number of vertices with different labels differs by at most one.Each vertex friendly labeling/induces an edge labeling f*E(G)→Z2,defined by f*(xy)=f(x)+f(y)for each xy∈E(G).Let er(i)=\{e∈E(G):f*(e)=i}|.The full friendly index set of G,denoted by FFI(G),is the set{ef*(1)-ep(0):f is friendly}.In this paper,we determine the full friendly index set of a family of cycle union graphs which are edge subdivisions of P2×Pn.展开更多
In this paper we will first give the characterization of the p^-low p^-degree,and prove that a p.r.e. degree(?)contains a p^-speedable set A if and only if(?)′>P(?)′.Then we classify the index sets of Low[n]~p an...In this paper we will first give the characterization of the p^-low p^-degree,and prove that a p.r.e. degree(?)contains a p^-speedable set A if and only if(?)′>P(?)′.Then we classify the index sets of Low[n]~p and High[n]~p and prove that Low [n]~p is Σ~P[n+3]-complete and High [n]~p is Σ~P [n+4]-complete.展开更多
为提高可消除项集的挖掘效率,在WPPC-Tree基础上提出优化后开始-结束序列树(start-finish-order tree,SFOTree),定义开始-结束序列集合(start-finish-order-set,SFO-Set)和开始-结束序列集合差(difference of start-finish-orderset,dSF...为提高可消除项集的挖掘效率,在WPPC-Tree基础上提出优化后开始-结束序列树(start-finish-order tree,SFOTree),定义开始-结束序列集合(start-finish-order-set,SFO-Set)和开始-结束序列集合差(difference of start-finish-orderset,dSFO-Set),建立项集的收益索引,提出一种基于dSFO-Set的可消除项集挖掘算法。利用dSFO-Set性质和收益索引,提高项集收益的计算效率,减少可消除项集的挖掘代价。分别在稠密数据集和稀疏模拟数据集上与传统算法进行测试比较,实验结果表明,该算法具有更好的挖掘效率。展开更多
In this paper, a sufficient condition for the existence of bifurcation points for discrete dynamical systems is presented. The relation between two families of systems is further discussed, and a sufficient condition ...In this paper, a sufficient condition for the existence of bifurcation points for discrete dynamical systems is presented. The relation between two families of systems is further discussed, and a sufficient condition for determining whether they may have the similar bifurcation points is given.展开更多
Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features ma...Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. In this paper, we propose a new unsupervised quick reduct （QR） algorithm using rough set theory. The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool. The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.展开更多
The preference analysis is a class of important issues in multi-criteria ordinal decision making.The rough set is an effective approach to handle preference analysis.In order to solve the multi-criteria preference ana...The preference analysis is a class of important issues in multi-criteria ordinal decision making.The rough set is an effective approach to handle preference analysis.In order to solve the multi-criteria preference analysis problems,this paper improves the preference relation rough set model and expands it to multi-granulation cases.Cost is also an important issue in the field of decision analysis.Taking the cost into consideration,we also expand the model to the cost sensitive multi-granulation preference relation rough set.Some theorems are represented,and the granule structure selection based on approximation quality is investigated.The experimental results show that the multi-granulation preference rough set approach with the consideration of cost has a better performance in granule structure selection than that without cost consideration.展开更多
The comprehensive evaluation method of enterprise core competitiveness is proposed by combining rough sets and gray correlation theories. Firstly,the initial index is screened through rough set attribute reduction alg...The comprehensive evaluation method of enterprise core competitiveness is proposed by combining rough sets and gray correlation theories. Firstly,the initial index is screened through rough set attribute reduction algorithm,and the evaluation weight of each index is obtained through the rough set theory. Then,based on the gray correlation theory, an evaluation model is built for empirical analysis. The 30 financial institutions on the Yangtze River Delta are examined from the theoretical and empirical perspective.The result demonstrates not only the feasibility of rough set attribute reduction algorithm in the core competitiveness index system of the financial institution,but also the accuracy of the combination of these two methods in the comprehensive evaluation of corporate core competitiveness.展开更多
Field data of outcrop spectrums provide important basis for modeling of hyper-spectral remote sensing aiming at mineral prospecting. We make an approach to the application of rough set theory in spectral discriminatio...Field data of outcrop spectrums provide important basis for modeling of hyper-spectral remote sensing aiming at mineral prospecting. We make an approach to the application of rough set theory in spectral discrimination of rocks. We build a decision table with an adequate number of samples (outcrops) of known rock type (the universe), of which the conditional attributes are discretized 'area spectrum absorption indexes' (ASAI) corresponding to wavelength intervals, and the decision attribute is rock type. We search to obtain the exhaustive set of reducts of the table, each of which will serve as a variable number of deduction rules. Suppose we have n (usually a very big number) rules in total and there are m types of rocks in our universe, for any unknown sample, we judge its rock type by each of those rules. An unknown sample may be recognized as a different type by different rules because it is outside our universe, and we accept the most frequent judgment result and ignore the other m-1 types of results. Our ASAI is an improvement upon the traditional spectrum absorption index (SAI), better applicable to field spectrums: given a spectrum curve and a wavelength interval, we take the average reflectance within the interval as a base line and let ASAI=a below/(a above+a below), where a below and a above stand for total areas, bounded by the curve, the base line and the borders of the intervalbelow and above the base line respectively. With the equipments of FieldSpectr Fr (made by ASD Co., US), we collected data from Baiya gold deposit, Yunnan, and applied the above method to discriminate altered rocks as an experiment. The results show satisfactory performance of the method.展开更多
文摘A vertex labeling f ： V →Z2 of a simple graph G = （V, E） induces two edge labelings The friendly index set and the product-cordial index set of G are defined as the setsf is friendly}. In this paper we study and determine the connection between the friendly index sets and product-cordial index sets of 2-regular graphs and generalized wheel graphs.
基金Supported by FRG/07-08/II-08 Hong Kong Baptist University
文摘Let G =（V, E） be a connected simple graph. A labeling f ： V → Z2 induces an edge labeling f＊ ： E → Z2 defined by f＊（xy） = f（x） ＋f（y） for each xy ∈ E. For i ∈ Z2, let vf（i） = ｜f^-1（i）｜ and ef（i） = ｜f＊^-1（i）｜. A labeling f is called friendly if ｜vf（1） - vf（0）｜ ≤ 1. For a friendly labeling f of a graph G, we define the friendly index of G under f by if（G） = e（1） - el（0）. The set [if（G） ｜ f is a friendly labeling of G} is called the full friendly index set of G, denoted by FFI（G）. In this paper, we will determine the full friendly index set of every Cartesian product of two cycles.
基金the National Natural Science Foundation of China （No. 10671005） the Natural Science Foundation of Hebei Province （No. A2007000230）.
文摘In this paper, we introduce the concept of the general butterfly graph B[m,n;d] for integers m,n ≥ 3, d ≥ 1, determine its balance index set, and give the necessary and sufficient condition for balanced graph B[m,n;d] to exist.
基金This work was supported partly by the National Natural Science Foundation of China(Grant Nos.11801149,11801148)S.Wu was also partially supported by the Doctoral Fund of Henan Polytechnic University(B2018-55).
文摘Let G be a connected simple graph with vertex set V(G)and edge set E(G).A binary vertex labeling f:V(G)→Z2,is said to be friendly if the number of vertices with different labels differs by at most one.Each vertex friendly labeling/induces an edge labeling f*E(G)→Z2,defined by f*(xy)=f(x)+f(y)for each xy∈E(G).Let er(i)=\{e∈E(G):f*(e)=i}|.The full friendly index set of G,denoted by FFI(G),is the set{ef*(1)-ep(0):f is friendly}.In this paper,we determine the full friendly index set of a family of cycle union graphs which are edge subdivisions of P2×Pn.
文摘In this paper we will first give the characterization of the p^-low p^-degree,and prove that a p.r.e. degree(?)contains a p^-speedable set A if and only if(?)′>P(?)′.Then we classify the index sets of Low[n]~p and High[n]~p and prove that Low [n]~p is Σ~P[n+3]-complete and High [n]~p is Σ~P [n+4]-complete.
文摘为提高可消除项集的挖掘效率,在WPPC-Tree基础上提出优化后开始-结束序列树(start-finish-order tree,SFOTree),定义开始-结束序列集合(start-finish-order-set,SFO-Set)和开始-结束序列集合差(difference of start-finish-orderset,dSFO-Set),建立项集的收益索引,提出一种基于dSFO-Set的可消除项集挖掘算法。利用dSFO-Set性质和收益索引,提高项集收益的计算效率,减少可消除项集的挖掘代价。分别在稠密数据集和稀疏模拟数据集上与传统算法进行测试比较,实验结果表明,该算法具有更好的挖掘效率。
基金Project supported by the National Natural Science Foundation of China (Grant No.10672146)the Shanghai Leading Academic Discipline Project (Grant No.S30104)
文摘In this paper, a sufficient condition for the existence of bifurcation points for discrete dynamical systems is presented. The relation between two families of systems is further discussed, and a sufficient condition for determining whether they may have the similar bifurcation points is given.
基金supported by the UGC, SERO, Hyderabad under FDP during XI plan periodthe UGC, New Delhi for financial assistance under major research project Grant No. F-34-105/2008
文摘Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. In this paper, we propose a new unsupervised quick reduct （QR） algorithm using rough set theory. The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool. The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.
基金supported in part by Natural Science Foundation of Education Department of Sichuan Province under Grant No.12ZA178Key Technology Support Program of Sichuan Province under Grant No.2015GZ0102+1 种基金Science and Technology Project of Chongqing Municipal Education Commission under Grant No.KJ1400407Chongqing Science and Technology Commission Project under Grant No.cstc2014jcyj A10051
文摘The preference analysis is a class of important issues in multi-criteria ordinal decision making.The rough set is an effective approach to handle preference analysis.In order to solve the multi-criteria preference analysis problems,this paper improves the preference relation rough set model and expands it to multi-granulation cases.Cost is also an important issue in the field of decision analysis.Taking the cost into consideration,we also expand the model to the cost sensitive multi-granulation preference relation rough set.Some theorems are represented,and the granule structure selection based on approximation quality is investigated.The experimental results show that the multi-granulation preference rough set approach with the consideration of cost has a better performance in granule structure selection than that without cost consideration.
文摘The comprehensive evaluation method of enterprise core competitiveness is proposed by combining rough sets and gray correlation theories. Firstly,the initial index is screened through rough set attribute reduction algorithm,and the evaluation weight of each index is obtained through the rough set theory. Then,based on the gray correlation theory, an evaluation model is built for empirical analysis. The 30 financial institutions on the Yangtze River Delta are examined from the theoretical and empirical perspective.The result demonstrates not only the feasibility of rough set attribute reduction algorithm in the core competitiveness index system of the financial institution,but also the accuracy of the combination of these two methods in the comprehensive evaluation of corporate core competitiveness.
基金ThisresearchisjointlysupportedbytheNationalNaturalScienceFoun dationofChina (No .4 0 2 72 0 2 2 )andtheKeyBrainstormProjectoftheMinistryofLandandResourcesofChina (No .2 0 0 1 0 30 5)
文摘Field data of outcrop spectrums provide important basis for modeling of hyper-spectral remote sensing aiming at mineral prospecting. We make an approach to the application of rough set theory in spectral discrimination of rocks. We build a decision table with an adequate number of samples (outcrops) of known rock type (the universe), of which the conditional attributes are discretized 'area spectrum absorption indexes' (ASAI) corresponding to wavelength intervals, and the decision attribute is rock type. We search to obtain the exhaustive set of reducts of the table, each of which will serve as a variable number of deduction rules. Suppose we have n (usually a very big number) rules in total and there are m types of rocks in our universe, for any unknown sample, we judge its rock type by each of those rules. An unknown sample may be recognized as a different type by different rules because it is outside our universe, and we accept the most frequent judgment result and ignore the other m-1 types of results. Our ASAI is an improvement upon the traditional spectrum absorption index (SAI), better applicable to field spectrums: given a spectrum curve and a wavelength interval, we take the average reflectance within the interval as a base line and let ASAI=a below/(a above+a below), where a below and a above stand for total areas, bounded by the curve, the base line and the borders of the intervalbelow and above the base line respectively. With the equipments of FieldSpectr Fr (made by ASD Co., US), we collected data from Baiya gold deposit, Yunnan, and applied the above method to discriminate altered rocks as an experiment. The results show satisfactory performance of the method.
