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高校科技活动指标静态聚类趋势分析研究 总被引：1，自引：0，他引：1
聚类分析用于高校科技活动的评价体现了简捷、实用的特点,是高校科技实力评估的一种可行的新途径.以江苏省15所部委和省属重点高校1998-2002年科技统计指标为例,用静态聚类的方法对一个连续时间段内的科技统计指标进行年度聚类分析,研究江苏省高校在一个时间段内的科技发展趋势. 相似文献
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以江苏省15所部委和省属重点高校1998-2002年科技统计指标为例,分别选取不同标准方案,利用动态聚类的分析方法进行分析,研究不同的标准方案对高校科技活动绩效的优劣排序产生的影响,从而探究高校科技统计指标动态聚类中的最佳参考方案。 相似文献
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利用灰色系统理论的灰色关联度分析、灰关联聚类的方法,以江苏省38所部委和省属高校2002年科技统计指标为例,进行分析、聚类,结果形成了38所高校科技活动绩效的优劣排序,与同类评价相比,本方法用于高校科技活动的评价体现了简捷、实用的特点,是高校科技实力评估的一种可行的新途径。 相似文献
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聚类方法可以用于高校图书馆数据挖掘.文章针对具体应用讨论了两种聚类方法,一种是需要提供类别数目参数的K-均值聚类方法,另一种是不需要提供类别数目参数的均值漂移聚类方法.根据图书馆数据挖掘的具体要求,采用不同的聚类方法能够更好地作出分析. 相似文献
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政策绩效评价是全面展现科技创新政策实施效果的重要手段。本文以江苏高校“科技改革30条”及配套制度等科技创新政策为研究对象，综合采用AHP-熵值法和K-means聚类分析法，从动态和静态两个角度对江苏五种不同类型高校“科技改革30条”政策绩效进行评价。结果显示，江苏高校“科技改革30条”政策实施取得良好成效，但高校间政策绩效存在较大不均衡性，部属高校和江苏高水平大学重点支持高校政策绩效水平远高于其他类型高校；科研评价和科技成果转化机制仍是江苏高校科技创新政策改革的重点和难点。基于以上研究，从坚持体制机制改革和差异化发展战略两个层面，提出政策优化建议。 相似文献
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针对传统的K-means算法运行的结果依赖于初始的聚类数目和聚类中心,本文提出了一种基于优化初始聚类中心的K-means算法.该算法通过量化样本间距离和聚类的紧密性来确定聚类数目K值;根据数据集的分布特征来选取相距较远的数据作为初始聚类中心,避免了传统K-means算法的聚类数目和聚类中心的随机选取.UCI机器学习数据... 相似文献
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企业积累了大量的客户消费数据,如何从大量的数据中发现用户的消费模式,对企业的营销策略具有重要的指导意义,数据挖掘技术正是可以从大量的数据中挖掘出对企业决策有价值的信息。针对客户数据的特点,提出一种基于Kruskal算法的最小生成树模糊聚类算法KTFC,并将其应用在客户关系管理中。实验证明,该模糊聚类算法可以有效地对企业客户群进行分类,并分析出每类客户的特点,动态地选取不同的A值可以获得不同的聚类结果,大大地提高了聚类的灵活性。 相似文献
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为解释国有大企业创新集聚能力与创新集聚绩效的内在关系,研究采用知识集成视角探讨创新集聚能力对创新集聚绩效的作用。基于典型国有大企业创新集聚多案例研究,研究识别出国有大企业多维创新集聚能力,并通过案例分析构建了创新集聚能力的作用过程模型,剖析了国有大企业创新集聚能力对创新集聚绩效的作用机理。研究结论显示,创新集聚全生命周期内国有大企业创新集聚能力与知识集成过程的高度匹配是提升创新集聚绩效的关键因素,研究在丰富创新集聚理论的同时为国有大企业创新集聚能力培育提供了借鉴。 相似文献
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一种基于密度最大值的聚类算法 总被引：1，自引：0，他引：1
提出了一种结合了基于密度聚类思想的划分聚类方法——"密度最大值聚类算法(MDCA)",以最大密度对象作为起始点,通过考察最大密度对象所处空间区域的密度分布情况来划分基本簇,并合并基本簇获得最终的簇划分.实验表明,MDCA能够自动确定簇数量,并有效发现任意形状的簇,对于未知数据集的处理能力和聚类准确度都优于传统的基于划分聚类算法. 相似文献
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Frizo Janssens Lin Zhang Bart De Moor Wolfgang Glnzel 《Information processing & management》2009,45(6):683-702
A hybrid text/citation-based method is used to cluster journals covered by the Web of Science database in the period 2002–2006. The objective is to use this clustering to validate and, if possible, to improve existing journal-based subject-classification schemes. Cross-citation links are determined on an item-by-paper procedure for individual papers assigned to the corresponding journal. Text mining for the textual component is based on the same principle; textual characteristics of individual papers are attributed to the journals in which they have been published. In a first step, the 22-field subject-classification scheme of the Essential Science Indicators (ESI) is evaluated and visualised. In a second step, the hybrid clustering method is applied to classify the about 8300 journals meeting the selection criteria concerning continuity, size and impact. The hybrid method proves superior to its two components when applied separately. The choice of 22 clusters also allows a direct field-to-cluster comparison, and we substantiate that the science areas resulting from cluster analysis form a more coherent structure than the “intellectual” reference scheme, the ESI subject scheme. Moreover, the textual component of the hybrid method allows labelling the clusters using cognitive characteristics, while the citation component allows visualising the cross-citation graph and determining representative journals suggested by the PageRank algorithm. Finally, the analysis of journal ‘migration’ allows the improvement of existing classification schemes on the basis of the concordance between fields and clusters. 相似文献
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《Information processing & management》2022,59(4):102949
This paper proposes a new method for semi-supervised clustering of data that only contains pairwise relational information. Specifically, our method simultaneously learns two similarity matrices in feature space and label space, in which similarity matrix in feature space learned by adopting adaptive neighbor strategy while another one obtained through tactful label propagation approach. Moreover, the above two learned matrices explore the local structure (i.e., learned from feature space) and global structure (i.e., learned from label space) of data respectively. Furthermore, most of the existing clustering methods do not fully consider the graph structure, they can not achieve the optimal clustering performance. Therefore, our method forcibly divides the data into c clusters by adding a low rank restriction on the graphical Laplacian matrix. Finally, a restriction of alignment between two similarity matrices is imposed and all items are combined into a unified framework, and an iterative optimization strategy is leveraged to solve the proposed model. Experiments in practical data show that our method has achieved brilliant performance compared with some other state-of-the-art methods. 相似文献
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《Information processing & management》2001,37(3):459-484
Satisfying non-trivial information needs involves collecting information from multiple resources, and synthesizing an answer that organizes that information. Traditional recall/precision-oriented information retrieval focuses on just one phase of that process: how to efficiently and effectively identify documents likely to be relevant to a specific, focused query. The TREC Interactive Track has as its goal the location of documents that pertain to different instances of a query topic, with no reward for duplicated coverage of topic instances. This task is similar to the task of organizing answer components into a complete answer. Clustering and classification are two mechanisms for organizing documents into groups. In this paper, we present an ongoing series of experiments that test the feasibility and effectiveness of using clustering and classification as an aid to instance retrieval and, ultimately, answer construction. Our results show that users prefer such structured presentations of candidate result set to a list-based approach. Assessment of the structured organizations based on the subjective judgement of the experiment subjects suggests that the structured organization can be more effective; however, assessment based on objective judgements shows mixed results. These results indicate that a full determination of the success of the approach depends on assessing the quality of the final answers generated by users, rather than on performance during the intermediate stages of answer construction. 相似文献
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《Information processing & management》2022,59(4):102967
Graph-based multi-view clustering aims to take advantage of multiple view graph information to provide clustering solutions. The consistency constraint of multiple views is the key of multi-view graph clustering. Most existing studies generate fusion graphs and constrain multi-view consistency by clustering loss. We argue that local pair-view consistency can achieve fine-modeling of consensus information in multiple views. Towards this end, we propose a novel Contrastive and Attentive Graph Learning framework for multi-view clustering (CAGL). Specifically, we design a contrastive fine-modeling in multi-view graph learning using maximizing the similarity of pair-view to guarantee the consistency of multiple views. Meanwhile, an Att-weighted refined fusion graph module based on attention networks to capture the capacity difference of different views dynamically and further facilitate the mutual reinforcement of single view and fusion view. Besides, our CAGL can learn a specialized representation for clustering via a self-training clustering module. Finally, we develop a joint optimization objective to balance every module and iteratively optimize the proposed CAGL in the framework of graph encoder–decoder. Experimental results on six benchmarks across different modalities and sizes demonstrate that our CAGL outperforms state-of-the-art baselines. 相似文献
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《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
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Larry E. Stanfel 《Information processing & management》1983,19(1):37-50
Given the difficulty of designing and creating information systems of many components and interconnections, it is commonplace to find these tasks accomplished by means of a partition into subsystems. Later the distinct subsystems are made to interface with one another and an overall system is achieved. The purpose of the present paper is to point out the availability of methods for effecting the partition in optimal or approximately optimal ways. Clustering algorithms for the specific case of information systems are obtained and exemplified. 相似文献