探索高校科技期刊出版模式的改革措施

在介绍了当前国际上科技期刊主流出版模式以及跨国化、集团化、规模化的发展趋势的基础上,对我国高校科技期刊的出版模式进行了探索和研究,分析其体制机制、运营方式及各模式优缺点,并对高校科技期刊的发展定位以及集约化改革措施提出了一些建议,为促进我国高校科技期刊整合资源、做大做强提供参考.     

基于模糊聚类的超链分析算法

基于关键字匹配的搜索引擎排序网页时仅仅考虑评价网页的重要性,而忽视分类;基于分类目录的搜索引擎很难动态分析Web信息。本文在分析它们不足的前提下,提出利用模糊聚类的方法对搜索引擎的检索结果进行动态分类,依据超链分析算法PageRank和Web文档隶属度相结合进行分类排序,并给出具有调节值的结合公式。实验证明,该算法能够更有效地满足用户的需要,提高检索效率。     

文本自动分类研究

本文简要介绍了国内外文本自动分类的发展概况，论述了自动分类的定义，自动归类和自动聚类的几种常见方法，并对应用实例进行了分析。     

图书馆学学科知识地图的构建

简要介绍了知识地图的概念及构建方法,通过对CSSCI2003-2005年数据的统计分析,构建了图书馆学关键词关联图、学科专家知识地图、学科交叉图等学科知识地图,并提出了应用聚类分析来构建图书馆学关键词聚类树状图的方法。     

NFCNNC:一种新的模糊竞争神经网络聚类模型及其在文本聚类中的应用

提出了一种新的模糊竞争神经网络聚类模型NFCNNC,并将其应用到文本聚类中。NFCNNC将模糊中心聚类(FCC)算法得到的模糊聚类中心向量作为神经网络的权值,通过比较隶属度值得到获胜神经元。网络中仅两个神经元同时调节权值。隶属度值最大的神经元以较大的学习率调整权值,隶属度次大的神经元以较小的学习率调整权值,其他神经元权值不变。按照FCC算法调整模糊聚类中心向量值(即权值)和神经元的隶属度,当网络稳定时,即可确定聚类数。与传统模糊神经网络模型相比,本文的模糊神经网络模型具有结构简单、运行效率高、聚类精度高的优点,同时克服了传统算法需预先指定聚类数的局限性。通过对文本聚类的实验验证,本算法取得了良好的效果。     

Web日志中用户存取模式的聚类研究

基于用户访问网页的不同序列反映了用户特定的兴趣,提出了Web日志中用户存取模式的聚类算法。利用传统的Leader算法只扫描数据集一遍的优点,以及粗糙理论在处理含有不确定信息问题上的优势,给出了结合粗糙理论的改进Leader算法对用户存取模式进行聚类方法,使得同一类中的用户存取模式尽可能的相近或相似,不同类中的模式尽可能的相异。实验结果表明,该算法在可承受的计算时间内可对Web日志中的用户存取模式进行有效聚类。     

Hierarchical clustering of a Finnish newspaper article collection with graded relevance assessments

Search facilitated with agglomerative hierarchical clustering methods was studied in a collection of Finnish newspaper articles (N = 53,893). To allow quick experiments, clustering was applied to a sample (N = 5,000) that was reduced with principal components analysis. The dendrograms were heuristically cut to find an optimal partition, whose clusters were compared with each of the 30 queries to retrieve the best-matching cluster. The four-level relevance assessment was collapsed into a binary one by (A) considering all the relevant and (B) only the highly relevant documents relevant, respectively. Single linkage (SL) was the worst method. It created many tiny clusters, and, consequently, searches enabled with it had high precision and low recall. The complete linkage (CL), average linkage (AL), and Ward's methods (WM) returned reasonably-sized clusters typically of 18–32 documents. Their recall (A: 27–52%, B: 50–82%) and precision (A: 83–90%, B: 18–21%) was higher than and comparable to those of the SL clusters, respectively. The AL and WM clustering had 1–8% better effectiveness than nearest neighbor searching (NN), and SL and CL were 1–9% less efficient that NN. However, the differences were statistically insignificant. When evaluated with the liberal assessment A, the results suggest that the AL and WM clustering offer better retrieval ability than NN. Assessment B renders the AL and WM clustering better than NN, when recall is considered more important than precision. The results imply that collections in the highly inflectional and agglutinative languages, such as Finnish, may be clustered as the collections in English, provided that documents are appropriately preprocessed.     

情报检索系统用户相关性判断的灰色聚类决策

所谓灰色聚类决策,就是按多个不同的决策指标对决策对象进行综合评价,以确定决策对象是否满足给定的取舍原则。该方法可以用于对情报检索系统的检索结果进行相关性判断。参考文献4。     

基于IIG和LSI组合特征提取方法的文本聚类研究

本文利用改进的信息增益特征选择方法和潜在语义索引技术组合的特征提取方法 ,对文本进行了有效的自动聚类。从语料库中抽取了 2 5 0篇文本 ,首先利用向量空间模型和改进的信息增益特征选择方法 ,构造文本特征向量 ,利用C 均值方法聚类 ,聚类结果准确率、查全率、F measure分别达到 0 .82、0 . 88、0 .83。在此基础上 ,对最优的特征选择结果运用潜在语义索引方法 ,对奇异值分解的结果进行截断处理 ,发现奇异值K取 4 0时聚类结果的准确率、查全率、F measure达到 0 . 95、0. 5 7、0 . 78,在有效地降维的同时 ,大幅度地提高了聚类的准确率。     

Comparison and Classification of Documents Based on Layout Similarity

This paper describes features and methods for document image comparison and classification at the spatial layout level. The methods are useful for visual similarity based document retrieval as well as fast algorithms for initial document type classification without OCR. A novel feature set called interval encoding is introduced to capture elements of spatial layout. This feature set encodes region layout information in fixed-length vectors by capturing structural characteristics of the image. These fixed-length vectors are then compared to each other through a Manhattan distance computation for fast page layout comparison. The paper describes experiments and results to rank-order a set of document pages in terms of their layout similarity to a test document. We also demonstrate the usefulness of the features derived from interval coding in a hidden Markov model based page layout classification system that is trainable and extendible. The methods described in the paper can be used in various document retrieval tasks including visual similarity based retrieval, categorization and information extraction.