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一种无向图的生成树算法 总被引:3,自引:1,他引:2
求无向图的生成树是在网络和回路分析中经常遇到的重要问题。文章描述采用计算树的方法求解无向图的生成树,这种方法是通过列举生成树之间的差别来实现的。 相似文献
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给出了矩阵同构变换、简单无向图距离矩阵、距离矩阵列和向量以及图的距离谱的定义, 将基于邻接矩阵的同构判定条件推广到简单无向图距离矩阵. 针对简单无向连通图的同构判定问题: 给出了基于距离矩阵特征多项式的同构判定条件; 进一步, 为避免计算误差对判定结果的影响, 给出了基于距离矩阵的秩与列和向量的同构判定条件. 上述两个判定条件均是充要条件且均具有多项式时间复杂度. 相似文献
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基于语义网的电子政务文档智能检索 总被引:7,自引:0,他引:7
根据电子政务文档的特点,通过电子政务主题词表计算检索文档集和检索请求的特征值。讨论了检索文档集和检索请求的相似性计算,从而找到与检索请求匹配的文档。根据电子政务文档元数据的语义组织形式,研究电子政务文档元数据的检索问题。对所检索到的文档进行元数据语义组织,从而在语义推理的基础上实现智能检索。 相似文献
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一个图是否为Hamilton图在于图中是否有Hamilton圈。文中提出了变换的方法来寻找图中的Hamilton圈,即在图的顶点集中寻找满足包含给定图中所有顶点的自归邻接边增长变换的方法来寻找给定图中的Hamilton圈。由此,设计了一个在Edmonds意义下的有效算法——自归邻接边增长算法(AEG)来寻找给定图中的自归邻接边增长变换,证明了该算法能正确判断给定简单无向图中有无Hamilton圈且时间复杂度为O(n^2)。最后通过应用实例说明该算法的有效性和实用性。 相似文献
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孙艳蕊 《小型微型计算机系统》2013,34(8)
图的极大独立集在计算机视觉、计算机网络、编码理论和资源配置等领域有着广泛的应用.本文利用图的分解方法给出了一个求简单无向图所有极大独立集的递归公式.定义了图的邻接矩阵的两个变换和点集合的一些运算.在此基础上,利用二分树给出了一个求无向图的所有极大独立集的有效算法.算法的时间复杂度是O(mn),其中m,n分别是图的所有极大独立集数和顶点个数.算法只需对网络的邻接矩阵进行处理,在计算机上实现起来非常方便.最后,通过实例验证了算法的有效性. 相似文献
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信息处理领域中,现有的各种文本分类算法大都基于向量空间模型,而向量空间模型却不能够有效地表达文档的结构信息,从而使得它还不能充分地表达文档的语义信息.为了更有效地表达文档的语义信息,本文首先提出了一种新的文档表示模型一图模型,即通过带权标号图表达文档的特征词条及其位置关联信息,在此基础上本文继而提出了一种新的文档相似性度量标准,并用于中文文本的分类.实验结果表明,基于图模型的这种文档表示方式是有效的和可行的. 相似文献
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从文档对象模型的角度对网页文档格式进行了研究,给出了基于文档对象获取的网页制作题自动阅卷方案,弥补了以往用文本匹配方式进行阋卷在知识点定位及取值过程中的不足。 相似文献
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Document similarity search is to find documents similar to a given query document and return a ranked list of similar documents to users, which is widely used in many text and web systems, such as digital library, search engine, etc. Traditional retrieval models, including the Okapi's BM25 model and the Smart's vector space model with length normalization, could handle this problem to some extent by taking the query document as a long query. In practice, the Cosine measure is considered as the best model for document similarity search because of its good ability to measure similarity between two documents. In this paper, the quantitative performances of the above models are compared using experiments. Because the Cosine measure is not able to reflect the structural similarity between documents, a new retrieval model based on TextTiling is proposed in the paper. The proposed model takes into account the subtopic structures of documents. It first splits the documents into text segments with TextTiling and calculates the similarities for different pairs of text segments in the documents. Lastly the overall similarity between the documents is returned by combining the similarities of different pairs of text segments with optimal matching method. Experiments are performed and results show: 1) the popular retrieval models (the Okapi's BM25 model and the Smart's vector space model with length normalization) do not perform well for document similarity search; 2) the proposed model based on TextTiling is effective and outperforms other models, including the Cosine measure; 3) the methods for the three components in the proposed model are validated to be appropriately employed. 相似文献
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P. Perumal 《计算机系统科学与工程》2022,43(1):203-218
With the wider growth of web-based documents, the necessity of automatic document clustering and text summarization is increased. Here, document summarization that is extracting the essential task with appropriate information, removal of unnecessary data and providing the data in a cohesive and coherent manner is determined to be a most confronting task. In this research, a novel intelligent model for document clustering is designed with graph model and Fuzzy based association rule generation (gFAR). Initially, the graph model is used to map the relationship among the data (multi-source) followed by the establishment of document clustering with the generation of association rule using the fuzzy concept. This method shows benefit in redundancy elimination by mapping the relevant document using graph model and reduces the time consumption and improves the accuracy using the association rule generation with fuzzy. This framework is provided in an interpretable way for document clustering. It iteratively reduces the error rate during relationship mapping among the data (clusters) with the assistance of weighted document content. Also, this model represents the significance of data features with class discrimination. It is also helpful in measuring the significance of the features during the data clustering process. The simulation is done with MATLAB 2016b environment and evaluated with the empirical standards like Relative Risk Patterns (RRP), ROUGE score, and Discrimination Information Measure (DMI) respectively. Here, DailyMail and DUC 2004 dataset is used to extract the empirical results. The proposed gFAR model gives better trade-off while compared with various prevailing approaches. 相似文献
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文档表示模型是文本自动处理的基础,是将非结构化的文本数据转化为结构化数据的有效手段。然而,目前通用的空间向量模型(Vector Space Model,VSM)是以单个的词汇为基础的文档表示模型,因其忽略了词间的关联关系,导致文本挖掘的准确率难以得到很大的提升。该文以词共现分析为基础,讨论了文档主题与词的二阶关系之间的潜在联系,进而定义了词共现度及与文档主题相关度的量化计算方法,利用关联规则算法抽取出文档集上的词共现组合,提出了基于词共现组合的文档向量主题表示模型(Co-occurrence Term based Vector Space Model, CTVSM),定义了基于CTVSM的文档相似度。实验表明,CTVSM能够准确反映文档之间的相关关系,比经典的文档向量空间模型(Vector Space Model,VSM)具有更强的主题区分能力。 相似文献