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.

Fuzzy c-means text clustering based on topic concept sub-space

To improve the accuracy of text clustering, fuzzy c-means clustering based on topic concept sub-space （TCS2FCM） is introduced for classifying texts. Five evaluation functions are combined to extract key phrases. Concept phrases, as well as the descriptions of final clusters, are presented using WordNet origin from key phrases. Initial centers and membership matrix are the most important factors affecting clustering performance. Orthogonal concept topic sub-spaces are built with the topic concept phrases representing topics of the texts and the initialization of centers and the membership matrix depend on the concept vectors in sub-spaces. The results show that, different from random initialization of traditional fuzzy c-means clustering, the initialization related to text content contributions can improve clustering precision.

Energy Optimization Oriented Three-Way Clustering Algorithm for Cloud Tasks

Cloud computing has developed as an important information technology paradigm which can provide on-demand services. Meanwhile,its energy consumption problem has attracted a grow-ing attention both from academic and industrial communities. In this paper,from the perspective of cloud tasks,the relationship between cloud tasks and cloud platform energy consumption is established and analyzed on the basis of the multidimensional attributes of cloud tasks. Furthermore,a three-way clustering algorithm of cloud tasks is proposed for saving energy. In the algorithm,f irst,t he cloud tasks are classified into three categories according to the content properties of the cloud tasks and resources respectively. Next,cloud tasks and cloud resources are clustered according to their computation characteristics（ e. g. computation-intensive,data-intensive）. Subsequently,greedy scheduling is performed. The simulation results showthat the proposed algorithm can significantly reduce the energy cost and improve resources utilization,compared with the general greedy scheduling algorithm.

Concept Association and Hierarchical Hamming Clustering Model in Text Classification

We propose two models in this paper. The concept of association model is put forward to obtain the co-occurrence relationships among keywords in the documents and the hierarchical Hamming clustering model is used to reduce the dimensionality of the category feature vector space which can solve the problem of the extremely high dimensionality of the documents' feature space. The results of experiment indicate that it can obtain the co-occurrence relations among key-words in the documents which promote the recall of classification system effectively. The hierarchical Hamming clustering model can reduce the dimensionality of the category feature vector efficiently, the size of the vector space is only about 10% of the primary dimensionality. Key words text classification - concept association - hierarchical clustering - hamming clustering CLC number TN 915. 08 Foundation item: Supporteded by the National 863 Project of China (2001AA142160, 2002AA145090)Biography: Su Gui-yang (1974-), male, Ph. D candidate, research direction: information filter and text classification.

The Research of an Incremental Conceptive Clustering Algorithm and Its Application in Detecting Money Laundering

Considering the constantly increasing of data in large databases such as wire transfer database, incremental clustering algorithms play a more and more important role in Data Mining （DM）. However, Few of the traditional clustering algorithms can not only handle the categorical data, but also explain its output clearly. Based on the idea of dynamic clustering, an incremental conceptive clustering algorithm is proposed in this paper. Which introduces the Semantic Core Tree （SCT） to deal with large volume of categorical wire transfer data for the detecting money laundering. In addition, the rule generation algorithm is presented here to express the clustering result by the format of knowledge. When we apply this idea in financial data mining, the efficiency of searching the characters of money laundering data will be improved.

Subspace Clustering in High-Dimensional Data Streams:A Systematic Literature Review

Clustering high dimensional data is challenging as data dimensionality increases the distance between data points,resulting in sparse regions that degrade clustering performance.Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space.Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams.Data streams are not only high-dimensional,but also unbounded and evolving.This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams.Although many articles have contributed to the literature review on data stream clustering,there is currently no specific review on subspace clustering algorithms in high-dimensional data streams.Therefore,this article aims to systematically review the existing literature on subspace clustering of data streams in high-dimensional streaming environments.The review follows a systematic methodological approach and includes 18 articles for the final analysis.The analysis focused on two research questions related to the general clustering process and dealing with the unbounded and evolving characteristics of data streams.The main findings relate to six elements:clustering process,cluster search,subspace search,synopsis structure,cluster maintenance,and evaluation measures.Most algorithms use a two-phase clustering approach consisting of an initialization stage,a refinement stage,a cluster maintenance stage,and a final clustering stage.The density-based top-down subspace clustering approach is more widely used than the others because it is able to distinguish true clusters and outliers using projected microclusters.Most algorithms implicitly adapt to the evolving nature of the data stream by using a time fading function that is sensitive to outliers.Future work can focus on the clustering framework,parameter optimization,subspace search techniques,memory-efficient synopsis structures,explicit cluster change detection,and intrinsic performance metrics.This article can serve as a guide for researchers interested in high-dimensional subspace clustering methods for data streams.

结合软约束的演化数据流模糊聚类算法

多源局部放电检测中,不同类型的局放信号同时存在且不断变化使得信号的分离更具挑战,而这种情况同样存在于许多数据流的聚类分析场景中。为了能够适应类簇内的不均匀密度和类簇间的重叠边界问题,同时对数据流的漂移和演化进行及时跟踪,提出了一种结合软约束的实时数据流模糊聚类算法。算法引入2种模糊性软约束来描述微簇距离和密度上的不确定度,通过阈值划分出核心微簇、边界微簇和离群微簇;在类簇边缘使用模糊隶属度,给予微簇分属不同类簇的可能性,保证类簇的完整性并提高聚类效果;使用两阶段的流程结构和2种时间窗口模型,赋予算法具有对可变化数据流的适应能力和更低的时间空间占用率。在多种数据集上的实验表明,该算法相比同类型算法在聚类效果上提升了1%~3%,且平均运行时间缩短5%~20%,在实际硬件平台的测试中也验证了算法的聚类分离性能。

基于快速康复理念的集束化护理方案对颈椎病手术患者术后康复的影响研究

目的探究基于快速康复理念的集束化护理方案对颈椎病手术患者术后康复的影响。方法方便选取2020年9月—2022年9月福建省泉州市第一医院骨科收治的116例颈椎病手术患者为研究对象,根据住院编号的单双数将其分为对照组(单数)和观察组(双数),每组58例。对照组实施常规护理,观察组实施基于快速康复理念的集束化护理,比较两组干预前后的颈椎活动度和生活质量。结果干预后,观察组患者颈椎前屈后伸、左右弯屈、左右旋转的活动度大于对照组,差异有统计学意义(P均<0.05);观察组颈椎病临床评价量表的主观症状、临床体征及适应能力评分及总分高于对照组,差异有统计学意义(P均<0.05)。结论颈椎病手术患者采用基于快速康复理念的集束化护理方案,可以促进其颈椎功能改善,提升其生活质量。

基于聚簇模型重用的概念漂移数据流半监督分类算法

带概念漂移的半监督数据流分类任务中,仅有少部分的数据被标记,这给分类器的训练、概念漂移的检测以及分类器对新概念的适应带来了巨大的挑战。现有的半监督聚簇分类算法仅对分类器池中的聚簇模型进行简单的增量更新,未能有效重用历史聚簇模型。因此,文中提出了一种新的聚簇模型重用的半监督分类算法,称为CDCMR。首先,数据流以数据块的形式到来,对数据块分完类后,训练一个簇数自适应确定的聚簇模型。其次,通过计算分类器池中的各组件分类器与聚簇模型之间的相似度,挑选多个组件分类器。再次,用当前数据块对挑选出来的组件分类器进行模型重用后,与聚簇模型集成。然后,将分类器池划分为新旧更替和多样性最大化分类器池进行更新。最后,对下一个数据块的样本进行集成分类。在多个人工和真实数据集上进行实验,结果表明,所提算法1)能有效适应概念漂移,与现有方法相比其性能有显著性提升。

基于改进模糊聚类算法的大数据随机挖掘仿真

大数据挖掘是从大量有噪声的、随机模糊的大数据中提取有价值信息的过程,由于海量大数据具有多维性、稀疏性以及动态性等特点,准确获取其分布特征的难度较大,随机挖掘难以直接实现。为此提出基于改进模糊聚类算法的大数据随机挖掘方法。利用建立的语义概念树模型获取大数据的特征分布关系,并根据模糊语义分析法得出大数据的语义相似性、关联性条件,提取大数据特征。优先确定最佳聚类数,采用改进模糊聚类算法对其聚类,实现基于改进模糊算法的大数据随机挖掘。实验结果表明,上述方法的大数据模糊聚类效果较好,随机挖掘准确率可达到95%以上,实验所得结果验证了上述方法较强的应用有效性。

基于异构网络语言形式背景的知识发现及规则提取

在不确定性环境下,如何处理具有复杂关系的数据是研究热点之一.网络形式背景将复杂网络分析和形式概念分析结合,为复杂关系数据的知识发现提供一种有效的数学工具.文中首先从网络结构的异构性出发,提出异构网络语言形式背景.异构网络包含专家给出的主观网络,又包含通过对象的特征挖掘的客观网络.然后,考虑网络的连通性,得到全局和局部异构网络语言概念,并给出异构网络下的全局连通及局部连通知识发现算法.最后,基于异构网络语言形式背景构建关联规则提取模型,通过实例验证知识发现及规则提取的合理性和有效性.

基于领域概念图的航天新闻自动摘要模型

互联网海量的航天新闻中隐含着大量航天情报信息,对其进行理解与压缩是提高后续情报分析效率的基础。然而通用的自动摘要算法往往会忽略很多航天领域关键信息,且有监督自动摘要算法需要对领域文本进行大量的数据标注,费时费力。因此,提出一种基于领域概念图的无监督自动摘要(DCG-TextRank)模型,利用领域术语辅助引导图排序,提高模型对领域文本的理解力。该模型分3个模块:领域概念图生成、图权重初始化、图排序及语义筛选。根据句向量相似度和领域术语库,将文本转换为包含句子节点和领域术语节点的领域概念图;根据航天新闻文本特征初始化领域概念图权值;采用TextRank模型对句子进行排序,并在语义筛选模块通过图节点聚类及设置摘要语义保留度的方法改进TextRank的输出,充分保留文本的多语义信息并降低冗余。所提模型具有领域可移植性,且实验结果表明:在航天新闻数据集中,所提模型相比传统TextRank模型性能提升了14.97%,相比有监督抽取式文本摘要模型BertSum和MatchSum性能提升了4.37%~12.97%。

一般信息系统的PoClustering与概念格

传统聚类方法生成的子集,一般来说都是不相交的.而严格的不相交分类结构,不能充分表现象本体这样的事物间丰富的类关系.在基因本体中,类与子类既不是简单的树也不是格结构,而是一个有向非循环图,其任何子女都可能有多个父结点.PoClustering是相异数据的一种无损聚类方法,概念格则反映了数据的对象和属性的对应关系.采用了PoClustering方法,在保持尽量多的信息的前提下建立一般数据集(信息系统)的属性确定下的概念化分类,讨论了它的算法,从概念格的角度研究了这种类的结构特征.

基于ESG理念的水生态PPP项目运营期绩效评价

PPP项目运营期绩效评价对保障项目健康合理运营、促进项目可持续发展具有重要意义。以ESG理念为基础建立水生态PPP项目运营期绩效评价指标体系,并综合使用熵权法和序关系分析法确定指标权重,结合灰色聚类评价模型确定绩效评价结果。最后,通过案例验证了该方法的可行性与适用性,希望为水生态PPP项目运营期的绩效评价提供有效支撑。

基于概念相似度的档案数据自动分类算法

针对现有分类算法存在的分类准确性差,并且分类过程中造成档案数据缺失的问题,引入概念相似度,开展档案数据自动分类算法研究。通过档案资源标记矩阵的建立,采集档案数据;结合概念相似度,对不确定分类数据进行聚类;引入融合学习技术,实现自动化分类档案数据。通过对比实验证明,文章算法的应用可保证档案数据分类的准确性,并且在分类过程中不会出现数据丢失的问题。

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.

Tools and Algorithm for Concept Analysis Using Medical Data

Drug taxonomy could be described as an inherent structure of different pharmaceutical componential drugs. Unfortunately, the literature does not always provide a clear path to define and classify adverse drug events. While not a systematic review, this paper uses examples from the literature to illustrate problems that investigators will confront as they develop a conceptual framework for their research. It also proposes a targeted taxonomy that can facilitate a clear and consistent approach to understanding different drugs and could aid in the comparison to results of past and future studies. In terms of building the drugs taxonomy, symptoms information were selected, clustered and adapted for this purpose. Finally, although national or international agreement on taxonomy for different drugs is a distant or unachievable goal, individual investigations and the literature as a whole will be improved by prospective, explicit classification of different drugs using this new pharmacy information system （PIS） and inclusion of the study＇s approach to classification in publications. The PIS allows user to find information quickly by following semantic connections that surround every drug linked to the subject. It provides quicker search, faster and more intuitive understanding of the focus. This research work can pretend to become a leading provider of encyclopedia service for scientists and educators, as well as attract the scientific community-universities, research and development groups.

基于OBE教育理念的高等数学课程群课程思政教学策略优化

高等数学课程群在高校公共必修课中占据着举足轻重的位置,随着当代信息技术日渐发展以及在教育领域的逐步渗透,高等数学课程群的教学模式的转变日新月异。近些年,课程思政在高等数学课程群教学中的受欢迎程度逐渐提高。然而,立足Outcome Based Education(OBE)教育理念,不难发现在高等数学课程群的思政教学中存在一些不足之处有待解决。文本以OBE教育理念为依据,结合高等数学课程群的教学现状,提出了高等数学课程群课程思政创新教学建议,以期完善OBE教育理念下的高等数学课程群课程思政教学体系。

结合微聚类和主动学习的流分类方法

数据流分类是数据挖掘中重要的研究内容,但是数据流中的概念漂移和标记成本昂贵的问题给分类带来了巨大的挑战。现有的研究工作大多采用基于主动学习的在线分类技术,一定程度上缓解了概念漂移和有限标签的问题,但是这些方法的分类效率较低,并且忽略了内存开销的问题。针对这些问题提出了一种结合微聚类和主动学习的流分类方法(a data stream classification method combining micro-clustering and active learning,CALC)。提出一种新的主动学习混合查询策略,将其与基于错误的表示学习相结合,从而在维护过程中衡量每个微聚类的重要性,通过动态维护一组微聚类以适应数据流中产生的概念漂移。采用基于微聚类的惰性学习方法,实现对数据流的分类,并完成对缓存微聚类的在线更新。使用三个真实数据集和三个人工合成数据集进行实验,结果显示CALC在分类准确率和内存开销方面优于现有的数据流分类算法。与基准模型(online reliable semi-supervised learning on evolving data streams,ORSL)相比,CALC的分类准确率有一定的提升,在六个数据集上的平均准确率分别提高了5.07、2.41、1.04、1.03、3.47、0.64个百分点。

属性聚类下三支概念的对比

三支概念分析是人工智能领域一个非常重要的研究方向,该理论最大的优势是可以同时研究形式背景中对象“共同具有”和“共同不具有”的属性。众所周知,经过属性聚类生成的新形式背景与原形式背景具有较强的联系,同时原三支概念与经过属性聚类得到的新三支概念也存在紧密的内在联系。为此,进行属性聚类下三支概念的对比研究和分析。首先基于属性聚类提出悲观属性聚类、乐观属性聚类以及一般属性聚类的概念,并研究了这三种属性聚类的关系;然后,通过对比聚类过程与三支概念形成的过程,研究了原三支概念与新三支概念的区别,分别从面向对象和面向属性的角度提出两个最低约束指数,探索了属性聚类对三支概念格的影响,进一步丰富了三支概念分析理论,为可视化数据处理领域提供了可行的思路。