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1.
A sequence of tests on derived polynomials to be strictly Hurwitz polynomials is shown to be equivalent to a given (typically real) polynomial having all its zeros in an open sector, symmetric with respect to the real axis, in the left half-plane. The number of tests needed is at most 1 + ?(ln k)/(ln 3)?, where k is the integer associated with the central angle π/k of the sector. An extension of this result on the sector as a region of root clustering is given which shows that only a limited number of tests are needed to verify that the roots are clustered in a region composed as the intersection of a set of primative (sector-like) regions. The results reported evolve from application of a collection of mappings on the complex plane defined by a particular collection of Schwarz-Christoffel transformations. 相似文献
2.
Ming Yin Zongze Wu Deyu Zeng Panshuo Li Shengli Xie 《Journal of The Franklin Institute》2018,355(8):3795-3811
In recent years, sparse subspace clustering (SSC) has been witnessed to its advantages in subspace clustering field. Generally, the SSC first learns the representation matrix of data by self-expressive, and then constructs affinity matrix based on the obtained sparse representation. Finally, the clustering result is achieved by applying spectral clustering to the affinity matrix. As described above, the existing SSC algorithms often learn the sparse representation and affinity matrix in a separate way. As a result, it may not lead to the optimum clustering result because of the independence process. To this end, we proposed a novel clustering algorithm via learning representation and affinity matrix conjointly. By the proposed method, we can learn sparse representation and affinity matrix in a unified framework, where the procedure is conducted by using the graph regularizer derived from the affinity matrix. Experimental results show the proposed method achieves better clustering results compared to other subspace clustering approaches. 相似文献
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4.
《Information processing & management》2016,52(4):618-627
Cluster analysis using multiple representations of data is known as multi-view clustering and has attracted much attention in recent years. The major drawback of existing multi-view algorithms is that their clustering performance depends heavily on hyperparameters which are difficult to set. In this paper, we propose the Multi-View Normalized Cuts (MVNC) approach, a two-step algorithm for multi-view clustering. In the first step, an initial partitioning is performed using a spectral technique. In the second step, a local search procedure is used to refine the initial clustering. MVNC has been evaluated and compared to state-of-the-art multi-view clustering approaches using three real-world datasets. Experimental results have shown that MVNC significantly outperforms existing algorithms in terms of clustering quality and computational efficiency. In addition to its superior performance, MVNC is parameter-free which makes it easy to use. 相似文献
5.
Data-driven fault diagnosis for an automobile suspension system by using a clustering based method 总被引：1，自引：0，他引：1
This paper concerns the issues of fault diagnosis and monitoring for an automobile suspension system where only accelerator sensors in the four corners of the car body are available. A clustering based method is proposed to detect the fault happened in the spring, and the Fisher discriminant analysis is applied to isolate the root factor for the fault. Different from most of the existing approaches, the pure data-driven characteristic enables this method to serve as an on-line fault diagnosis and monitoring tool without suspension model or fault features known as a prior. Moreover, this method can classify different reductions in the spring coefficient into one fault rather than different faults. The effectiveness of the proposed method is finally illustrated on an automobile suspension benchmark. 相似文献
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【目的/意义】针对基于关键词的科技文献聚类研究进行了一些探讨,包括：使用具有不同特征的关键词来
实现文献聚类在效果上有何差异;如何按特征对关键词进行选择来提高文献聚类效果。【方法/过程】按照关键词词
频与语义类型特征设置对照组进行实证研究,观察其对文献聚类密度及文献语义表示效果的影响。【结果/结论】单
独使用具有超高频、次高频、研究主题或限定范围特征的关键词进行文献聚类能使聚类密度较为合适;超高频特征
通常在其他频次中都具有体现,次高频词能同时反映不同频次的关键词特征,但次高频词对中频词特征的表示不
够全面;将语义类型不同的关键词分开来实现文献聚类,其效果好于将关键词进行组配,语义类型不同的关键词间
存在互斥性。【创新/局限】本文发现了在以关键词间的共现关系为基础来进行文献聚类时单独选择次高频或某一
语义类别的关键词来实现文献聚类具有较好效果,但缺少对关键词间语义结构关系的进一步研究。 相似文献
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In information retrieval, cluster-based retrieval is a well-known attempt in resolving the problem of term mismatch. Clustering requires similarity information between the documents, which is difficult to calculate at a feasible time. The adaptive document clustering scheme has been investigated by researchers to resolve this problem. However, its theoretical viewpoint has not been fully discovered. In this regard, we provide a conceptual viewpoint of the adaptive document clustering based on query-based similarities, by regarding the user’s query as a concept. As a result, adaptive document clustering scheme can be viewed as an approximation of this similarity. Based on this idea, we derive three new query-based similarity measures in language modeling framework, and evaluate them in the context of cluster-based retrieval, comparing with K-means clustering and full document expansion. Evaluation result shows that retrievals based on query-based similarities significantly improve the baseline, while being comparable to other methods. This implies that the newly developed query-based similarities become feasible criterions for adaptive document clustering. 相似文献
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We consider a challenging clustering task: the clustering of multi-word terms without document co-occurrence information in order to form coherent groups of topics. For this task, we developed a methodology taking as input multi-word terms and lexico-syntactic relations between them. Our clustering algorithm, named CPCL is implemented in the TermWatch system. We compared CPCL to other existing clustering algorithms, namely hierarchical and partitioning (k-means, k-medoids). This out-of-context clustering task led us to adapt multi-word term representation for statistical methods and also to refine an existing cluster evaluation metric, the editing distance in order to evaluate the methods. Evaluation was carried out on a list of multi-word terms from the genomic field which comes with a hand built taxonomy. Results showed that while k-means and k-medoids obtained good scores on the editing distance, they were very sensitive to term length. CPCL on the other hand obtained a better cluster homogeneity score and was less sensitive to term length. Also, CPCL showed good adaptability for handling very large and sparse matrices. 相似文献
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Document clustering is an important tool for document collection organization and browsing. In real applications, some limited knowledge about cluster membership of a small number of documents is often available, such as some pairs of documents belonging to the same cluster. This kind of prior knowledge can be served as constraints for the clustering process. We integrate the constraints into the trace formulation of the sum of square Euclidean distance function of K-means. Then,the combined criterion function is transformed into trace maximization, which is further optimized by eigen-decomposition. Our experimental evaluation shows that the proposed semi-supervised clustering method can achieve better performance, compared to three existing methods. 相似文献
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《Information processing & management》2020,57(2):102034
Methods for document clustering and topic modelling in online social networks (OSNs) offer a means of categorising, annotating and making sense of large volumes of user generated content. Many techniques have been developed over the years, ranging from text mining and clustering methods to latent topic models and neural embedding approaches. However, many of these methods deliver poor results when applied to OSN data as such text is notoriously short and noisy, and often results are not comparable across studies. In this study we evaluate several techniques for document clustering and topic modelling on three datasets from Twitter and Reddit. We benchmark four different feature representations derived from term-frequency inverse-document-frequency (tf-idf) matrices and word embedding models combined with four clustering methods, and we include a Latent Dirichlet Allocation topic model for comparison. Several different evaluation measures are used in the literature, so we provide a discussion and recommendation for the most appropriate extrinsic measures for this task. We also demonstrate the performance of the methods over data sets with different document lengths. Our results show that clustering techniques applied to neural embedding feature representations delivered the best performance over all data sets using appropriate extrinsic evaluation measures. We also demonstrate a method for interpreting the clusters with a top-words based approach using tf-idf weights combined with embedding distance measures. 相似文献
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共词聚类分析是情报学中进行学科热点探测、掌握学科发展脉络的一种主要方法,目前已经比较成熟得到了广泛的应用。Autonomy公司开发的autonomy智能搜索系统也同样具备专题聚类的功能,本文对该系统专题聚类的原理以及功能进行了阐述,并用CSCD的试验数据对系统的聚类功能进行测试。通过对试验结果的分析和解释,证明了autonomy系统的专题聚类功能具有一定的应用价值,可以与其他聚类方法结合起来,对探测学科热点提供一定的帮助。 相似文献
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主要研究了均值聚类图像分割问题。针对传统的聚类图像分割算法对图像地分割精度较低等问题,提出一种基于模糊控制的C-均值聚类快速图像分割新方法。本文采用快速模糊C-均值聚类算法对图像分割。实验结果表明,图像分割边缘清晰,分割效果明显优于传统的聚类图像分割算法。 相似文献
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《Information processing & management》2002,38(4):559-582
Hierarchic document clustering has been widely applied to information retrieval (IR) on the grounds of its potential improved effectiveness over inverted file search (IFS). However, previous research has been inconclusive as to whether clustering does bring improvements. In this paper we take the view that if hierarchic clustering is applied to search results (query-specific clustering), then it has the potential to increase the retrieval effectiveness compared both to that of static clustering and of conventional IFS. We conducted a number of experiments using five document collections and four hierarchic clustering methods. Our results show that the effectiveness of query-specific clustering is indeed higher, and suggest that there is scope for its application to IR. 相似文献
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《Research Policy》2023,52(8):104837
Global productivity growth has either stagnated or declined, despite continued technological innovations with the rise of knowledge-intensive intangibles that arise from the growth of knowledge stock (R&D activities). Understanding the root causes of this paradox in the context of growing economies requires an investigation of whether local knowledge diffusion can explain firm-level productivity differences, including key constraining factors like sources of financing or corporate governance structure. Using financial data of 7970 Indian firms over a 20-year period and clustering firms across industries, we assess the impact of R&D stock that is external to the firm through estimating both within (intra) and between (inter) industry spillovers. We find that both R&D and non-R&D-performing firms benefit from ‘between industry’ spillovers. We further show that firms with better access to finance achieve higher productivity, not only through their own R&D capital stock but also via both types of industry-level knowledge spillover. We allow for the two key sources of international spillovers namely import intensity and FDI. While import-intensive firms experience lower productivity, FDI mitigates this adverse productivity effect across knowledge-intensive exporting firms. The paper concludes that financially unconstrained firms and firms with greater corporate board connectedness derive positive industry-level spillover effects, reflecting intra- and inter-industry as domestic spillover or local value-chain effect in the literature on technological innovation. 相似文献
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[目的/意义]通过实验分析不同特征提取算法对新闻文本聚类效果的影响。[方法/过程]选取搜狗实验室的搜狐新闻语料库以及澳大利亚广播公司2003-2017年间的新闻标题语料库,对TF-IDF、Word2vec以及Doc2vec三种单一特征,TF-IDF+Word2vec、TF-IDF+Doc2vec、Word2vec+Doc2vec以及TF-IDF+Word2vec+Doc2vec四种组合特征在K-means、凝聚以及DBSCAN算法上分别进行聚类分析,通过Purity以及NMI两个评测指标对聚类效果进行评价。[结果/结论]单类特征中三个特征的聚类质量呈Word2vec> TF-IDF> Doc2vec关系;组合特征中TF-IDF+Word2vec的效果最优。Word2vec在单一特征中的表现最优,其也是不同组合特征间差异的主要因素,特征组合是否可以提升聚类性能需基于多因素进行综合判定。 相似文献
16.
将知识密集型服务业细分为四类子行业,运用区位熵的方法测算行业整体集聚度和各子行业的集聚水平,并以浙江省2008--2017年的地级市面板数据为样本,通过多元回归计量模型研究知识密集型服务业整体集聚和各子行业集聚对制造业结构升级的影响.实证结果显示,知识密集型服务业整体集聚明显有利于制造业结构升级,而各个子行业集聚对制造结构升级的影响有所不同. 相似文献
17.
针对传统的微博聚类分析中,只单独针对微博阅读数、评论数等数据(下称微博结构化数据)进行分类或者单独针对由微博内容进行文本分词得到的分词数据(下称微博分词)进行分类的问题,本文采用了Kohonen聚类,研究结合微博结构化数据和微博分词的融合数据聚类的效果是否比单独对微博结构化数据或对微博分词聚类有所提高。实证数据实验结果显示,微博结构化数据单独聚类会出现一个类的标准差特别大(本文称为离群类),而对融合数据聚类,微博结构化数据则不会出现离群类;融合数据聚类结果对微博分词的影响不显著。 相似文献
18.
Muammer Ozer 《Research Policy》2007,36(9):1372-1387
Addressing the demand uncertainties at the fuzzy-front-end of developing new online services, this paper tests the roles of numerous cluster-based methodologies in improving the predictive accuracy of consumer opinions. The results with an online service revealed that both crisp and non-crisp clustering methodologies improve the predictive accuracy and hence reduce the demand uncertainties at the fuzzy-front-end of the new product development process. They also showed that non-crisp clustering increases the accuracy more than does crisp clustering. Implications of the findings for our understanding of the earlier stages of the new product development process and for making informed R&D policies are discussed. 相似文献
19.
研究文本快速准确分类的问题。同一词语在不同的语言环境下或者由不同的人使用可能代表不同的含义,这些词语在文本分类中的描述特征却极为相似。传统的文本分类方法是将文本表示成向量空间模型,向量空间模型只是从词语的出现频率角度构造,当文中出现一些多义词和同义词时就会出现分类延时明显准确性不高等特点。为此提出一种基于语义索引的文本主题匹配方法。将文本进行关键词的抽取后构造文档-词语矩阵,SVD分解后通过优化平衡的方法进行矩阵降维与相似度的计算,克服传统方法的弊端。实践证明,这种方法能大幅度降低同义词与多义词对文本分类时的影响,使文本按主题匹配分类时准确高效,实验效果明显提高。 相似文献
20.
共生理论视角下产业集群式转移演进过程机理研究 总被引：1，自引：0，他引：1
在当今新一轮产业转移浪潮中,集群式转移——一种“企业的抱团迁徙”已经成为产业转移主导模式.企业之所以抱团迁徙,是由于在现代经济集群化发展背景下,企业在集群中形成了强共生关系,这种共生关系与生物种群之间的共生关系极其相似,产业集群式转移类似于生物群落的共生迁徙.本文引入共生理论,分析了产业集群式转移与生物群落共生迁徙的相似性,构建出产业集群式转移一般演进过程DLSN模型,在此基础上,探讨了产业集群式转移演进过程的阶段特征与条件,并通过案例作进一步验证,从而揭示产业集群式转移的基本规律,为制定产业转移引导政策提供理论依据. 相似文献