Rule acquisition of three-way semi-concept lattices in formal decision context

Three-way concept analysis is an important tool for information processing,and rule acquisition is one of the research hotspots of three-way concept analysis.However,compared with three-way concept lattices,three-way semi-concept lattices have three-way operators with weaker constraints,which can generate more concepts.In this article,the problem of rule acquisition for three-way semi-concept lattices is discussed in general.The authors construct the finer relation of three-way semi-concept lattices,and propose a method of rule acquisition for three-way semi-concept lattices.The authors also discuss the set of decision rules and the relationships of decision rules among object-induced three-way semi-concept lattices,object-induced three-way concept lattices,classical concept lattices and semi-concept lattices.Finally,examples are provided to illustrate the validity of our conclusions.

Identification and Prediction of Interdisciplinary Research Topics: A Study Based on the Concept Lattice Theory

Purpose: Formal concept analysis(FCA) and concept lattice theory(CLT) are introduced for constructing a network of IDR topics and for evaluating their effectiveness for knowledge structure exploration.Design/methodology/approach: We introduced the theory and applications of FCA and CLT, and then proposed a method for interdisciplinary knowledge discovery based on CLT. As an example of empirical analysis, interdisciplinary research(IDR) topics in Information & Library Science(LIS) and Medical Informatics, and in LIS and Geography-Physical, were utilized as empirical fields. Subsequently, we carried out a comparative analysis with two other IDR topic recognition methods.Findings: The CLT approach is suitable for IDR topic identification and predictions.Research limitations: IDR topic recognition based on the CLT is not sensitive to the interdisciplinarity of topic terms, since the data can only reflect whether there is a relationship between the discipline and the topic terms. Moreover, the CLT cannot clearly represent a large amounts of concepts.Practical implications: A deeper understanding of the IDR topics was obtained as the structural and hierarchical relationships between them were identified, which can help to get more precise identification and prediction to IDR topics.Originality/value: IDR topics identification based on CLT have performed well and this theory has several advantages for identifying and predicting IDR topics. First, in a concept lattice, there is a partial order relation between interconnected nodes, and consequently, a complete concept lattice can present hierarchical properties. Second, clustering analysis of IDR topics based on concept lattices can yield clusters that highlight the essential knowledge features and help display the semantic relationship between different IDR topics. Furthermore, the Hasse diagram automatically displays all the IDR topics associated with the different disciplines, thus forming clusters of specific concepts and visually retaining and presenting the associations of IDR topics through multiple inheritance relationships between the concepts.

Reconstructing Concept Lattices Using nth-Order Context Kernels

To be different from traditional algorithms for concept lattice constructing, a method based on nth-order context kernel is suggested in this paper. The context kernels support generating small lattices for sub-contexts split by a given context. The final concept lattice is reconstructed by combining these small lattices. All relevant algorithms are implemented in a system IsoFCA. Test shows that the method yields concept lattices in lower time complexity than Godin algorithm in practical case.

Concept Lattice Construction through the Composition and Decomposition of Formal Contexts

The purpose of this paper is to study the construction of concept lattice from variable formal contexts.Composition and decomposition theories are proposed for the unraveling of concept lattice from contexts with variable attribute set in the process of information updating.The relationship between the extension sets of the original context and that of its sub-context is analyzed.The composition and decomposition theories are then generalized to the situation involving more than two sub-contexts and the situation with variable attribute set and object set.

Rough Control Rule Mining Model Based on Decision Interval Concept Lattice and Its Application

Fusing the structure feature of interval concept lattice and the actual needs of rough control rules,we have constructed the decision interval concept lattice,further more,we also have built a rules mining model of rough control based on decision interval concept lattice,in order to achieve the optimality between rough control mining cost and control efficiency.Firstly,we have preprocessed the collected original data,so that we can transform it into Boolean formal context form,and then we have constructed the decision interval concept lattice in rough control;secondly,we have established the control rules mining algorithm based on decision interval concept lattice.By analyzing and judging redundant rules,we have formed the rough control association rule base in end.Analysis shows that under the premise of improving the reliability of rules,we have achieved the rough control optimization goal between cost and efficiency.Finally,the model of reservoir scheduling has verified its feasibility and efficiency.

SPPC注入器链中SS环Lattice的初步研究

SPPC项目对对撞机和高能物理发展有重大意义.作为SPPC注入器链的末端,SS环将质子能量从180GeV加速到2.1TeV,其性能与SPPC束流品质密切相关.为此初步探究了可用于SS环的Lattice结构.首先,讨论了SPPC整体设计需求对SS环填充模式的要求.在此基础上,使用了MADX程序,设计了两种Lattice结构:SSH和SSV,分别用于水平和垂直方向在快轴引出.分析得到SSH和SSV的横向闭轨偏离分别小于0.432mm和0.337mm.目前得到的Lattice结构可以作为下一步研究工作的基础.

面向知识结构分析的模糊概念格模型

知识空间理论使用数学语言对学习者进行知识评价与学习指导,属于数学心理学的研究范畴.技能与问题是构成知识空间的两个基本要素,深入研究两者之间的关系是知识状态刻画与知识结构分析的内在要求.在当前的知识空间理论研究中,没有明确建立技能与问题之间的双向映射,从而难以提出直观概念意义下的知识结构分析模型,也没有明确建立知识状态之间的偏序关系,不利于刻画知识状态之间的差异,更不利于规划学习者未来的学习路径.此外,现有的成果主要集中在经典的知识空间,没有考虑实际问题中数据的不确定性.为此,将形式概念分析与模糊集引入知识空间理论,建立面向知识结构分析的模糊概念格模型.具体地,分别建立知识空间与闭包空间的模糊概念格模型.首先,建立知识空间模糊概念格,并通过任意两个概念的上确界证明所有概念的外延构成知识空间.引入粒描述的思想定义技能诱导的问题原子粒,由问题原子粒的组合判定一个问题组合是否是知识空间中的一个状态,进而提出由问题组合获取知识空间模糊概念的方法.其次,建立闭包空间模糊概念格,并通过任意两个概念的下确界证明所有概念的外延构成闭包空间.类似地,定义问题诱导的技能原子粒,由技能原子粒的组合判定一个技能组合是否是闭包空间中某一知识状态所需的技能,进而提出由技能组合获取闭包空间模糊概念的方法.最后,通过实验分析问题数量、技能数量、填充因子以及分析尺度对知识空间与闭包空间规模的影响.结论表明知识空间模糊概念不同于现有的任何概念,也不能从其他概念派生而来.闭包空间模糊概念本质上是一种面向属性单边模糊概念.在二值技能形式背景中,知识空间与闭包空间中的状态具有一一对应关系,但这种关系在模糊技能形式背景中并不成立.

决策形式背景基于OE-协调性的属性约简

属性约简作为形式概念分析中非常重要的一个研究分支,在三支概念分析中也同样重要。基于对象导出三支概念格,提出了保持决策形式背景OE-协调性的属性约简理论,丰富了三支概念分析的约简理论。首先,定义了决策形式背景的OE-协调集和OE-约简,并将属性按其特征分为3类。其次,指出OE-约简的本质就是极小OE-协调集,给出了OE-协调集的几个判定定理,通过研究OE-协调集的充要条件,获取OE-约简的判定定理。最后,给出OE-差别矩阵和OE-差别函数的定义,并给出了利用OE-差别矩阵和OE-差别函数计算OE-约简的方法。

基于GIS辅助的多源地理空间矢量数据挖掘方法

噪声数据干扰下,数据挖掘精确度和效率偏低,为了提高数据挖掘结果的准确性,提出一种GIS辅助下多源地理空间矢量数据挖掘算法。通过非下采样小波变换方法对原始多源地理空间矢量数据多尺度分解,确保噪声和数据得到有效分离,通过变分偏微方程平滑数据内残余的噪声,避免数据内大量细节信息丢失。将GIS应用到多源地理空间矢量数据挖掘中,建立数据挖掘模型,并根据图层之间的关系变换数据,实现多源地理空间矢量数据的离散化处理。利用形式背景构建概念格和Hasse图,根据概念格中的内涵缩减集形成非冗余关联规则,结合数据挖掘模型完成数据挖掘。实验结果表明,所提方法可以有效滤除多源地理空间矢量数据中的噪声,获取满意的数据挖掘效果。

一种多线程概念约简算法

针对现有概念约简算法计算过程较为繁琐且执行效率低的问题,提出了一种多线程概念约简算法MTCR。MTCR以提高计算概念约简的效率为首要目标,在多核环境下利用多线程技术并行计算概念约简。首先,MTCR算法使用两个线程分别计算单个对象的对象代表概念集和单个属性的属性代表概念集;然后,将形式背景中的对象(属性)依次放入p个队列,并为每个队列创建线程;最后,通过多线程方式并行计算任意对象的对象代表概念集和任意属性的属性代表概念集,以及两类代表概念集的交集,进而构建代表概念矩阵,据此计算出所有概念约简。在MTCR算法中,使用多线程的每个阶段的数据相对独立,使得多线程之间不需要频繁的同步操作,从而减少了线程之间的竞争和等待。这样可充分有效地利用计算资源,大大提升算法的性能。UCI数据集和随机数据集上的实验表明:MTCR算法可以准确得到概念约简结果,在使用单线程情况下执行速度与串行概念约简算法SCR相近;当线程数不超过8时,线程数每增加1倍,MTCR算法执行速度可提高30%以上。

面向属性概念格下的知识状态转移方法

如何高效率地寻找猜测答对或粗心答错情况下不相容答题状态对应受测者真实的知识状态是目前计算机自适应测试领域研究的一个热点问题.现有知识状态转移方法时间成本高,不适用于含有大量测试问题和技能的应用场景.并且,现有知识状态转移方法将合取模型和能力模型均视为能力模型,采用同一种方法进行知识状态转移,灵活性不够,评估效率不高.本文针对合取模型和能力模型,分别提出基于面向属性概念格的知识状态转移方法.首先由技能映射或多映射建立技能背景并诱导出对应的面向属性概念格,进一步使用概念格中概念的外延并结合最大概率方法得到受测者猜测或粗心情况下的真实知识状态,最后给出知识评估过程.实验表明,在知识评估结果相同的前提下,本文所提方法相比现有方法极大地降低了评估时间成本,因此具有更好的性能.

基于对象阈值变精度概念的启发式推荐方法

针对启发式概念推荐方法进行优化,提出一种基于对象阈值变精度概念的启发式推荐方法。将模糊形式背景转化为对象阈值可变的变精度形式背景,在此形式背景的基础上定义概念构造的启发式信息;利用内涵约束,在保证群组相似度的基础上,构造当前加权面积最大的概念,将这些概念组成强概念集合;在这个概念集合中利用组推荐方法对所有用户进行推荐。实验在MovieLens系列数据集和FilmTrust数据集上的一系列结果验证这种优化是有效的,能够大幅加强推荐的精度和性能。

基于依赖空间的对象导出三支概念格的属性约简

在给定形式背景下,基于依赖空间提出了对象导出三支概念格属性约简的新定义,在此基础上讨论了属性约简方法。首先,借助三支算子定义了对象幂集上的一致关系,引入依赖空间。通过依赖空间生成闭包算子的闭元素,研究了闭元素和对象导出三支概念的外延之间的关系。其次,提出基于依赖空间的对象导出三支概念格属性约简的定义,该定义下的约简集是保持由原属性集确定的一致关系不变的最小属性子集。最后,给出协调集的判定定理,利用可辨识属性矩阵及辨识函数给出对象导出三支概念格的属性约简方法。

面向属性概念格的概念特征与概念约简

提出面向属性概念协调集和概念约简,定义了包含补二元关系的面向属性概念极小集,讨论了面向属性概念格中核心概念、相对必要概念及不必要概念的概念特征,并研究了三种概念的判别方法.给出了面向属性概念约简的可行方法及其相应算法.

Role based access control design using Triadic concept analysis

Role based access control is one of the widely used access control models.There are investigations in the literature that use knowledge representation mechanisms such as formal concept analysis(FCA),description logics,and Ontology for representing access control mechanism.However,while using FCA,investigations reported in the literature so far work on the logic that transforms the three dimensional access control matrix into dyadic formal contexts.This transformation is mainly to derive the formal concepts,lattice structure and implications to represent role hierarchy and constraints of RBAC.In this work,we propose a methodology that models RBAC using triadic FCA without transforming the triadic access control matrix into dyadic formal contexts.Our discussion is on two lines of inquiry.We present how triadic FCA can provide a suitable representation of RBAC policy and we demonstrate how this representation follows role hierarchy and constraints of RBAC on sample healthcare network available in the literature.

Realization of Rough Set Approximation Toplogical Operations Based on Formal Concept Analysis

There is an intimate correlation between rough set theory and formal concept analysis theory, so rough set approximations can be realized by means of formal concept analysis. For any given multiple valued information system, the realization of rough set approximation operation has two major steps, firstly convert the information system from multiple valued one to single valued formal context, secondly realize rough set approximation operations aided by concept lattice, which is equivalent to a query operation under some necessary conditions.

冲突分析的研究进展

冲突广泛存在于社会生活的各个方面,冲突分析受到越来越多的关注。由于冲突分析的研究仍然处于初始与发展阶段,为了促进冲突分析的研究与发展,本研究对冲突分析的理论与方法进行梳理和总结,并指出冲突分析研究中存在的若干问题,提出解决问题的初步研究设想,为冲突分析的后续研究提供有益的参考。

广义后缀树的概念生成算法

提出一种基于广义后缀树的概念生成算法(generalized suffix tree based concept generation algorithm,GSTCG),将背景中所有对象的属性序列及其后缀建立为一棵广义后缀树,并根据广义后缀树产生候选概念;其次,合并具有相同对象集合的候选概念,再根据规则对候选概念进行扩展;最后,删除冗余的候选概念后得到全部形式概念。在两类不同参数人工数据集上的实验结果表明,GSTCG算法与NextClosure算法在所有背景上得到的概念数量一致,且前者具有更优的时间性能。

基于用户兴趣概念格约简的推荐评分预测算法

数据稀疏性制约了推荐系统的性能,而合理填充评分矩阵中的缺失值可以有效提升预测的准确性。因此,提出一种基于用户兴趣概念格约简的推荐评分预测(RRP-CLR)算法。该算法包含近邻选择和评分预测两个模块,分别负责生成精简最近邻集合和实现评分预测及推荐。近邻选择模块将用户评分矩阵转化为二进制矩阵后作为用户兴趣形式背景,提出了形式背景约简规则和概念格冗余概念删除规则,以提高生成精简最近邻的效率;在评分预测模块利用新提出的用户相似度计算方法,消除用户主观因素造成的评分差异对相似度计算的影响,而且当两个用户共同评分项目数小于特定阈值时,适当缩放相似度,使用户间的相似度与真实情况更吻合。实验结果表明,与使用皮尔逊相关系数的基于用户的协同过滤推荐算法(PC-UCF)及基于用户兴趣概念格的推荐评分预测方法(RRP-UICL)相比,RRP-CLR算法的平均绝对误差(MAE)和均方根误差(RMSE)更小,具有更好的评分预测准确率和稳定性。