A citation-based document retrieval system for finding research expertise     

Quan Thanh Tho  S.C. Hui  A.C.M. Fong 《Information processing & management》2007

Current citation-based document retrieval systems generally offer only limited search facilities, such as author search. In order to facilitate more advanced search functions, we have developed a significantly improved system that employs two novel techniques: Context-based Cluster Analysis (CCA) and Context-based Ontology Generation frAmework (COGA). CCA aims to extract relevant information from clusters originally obtained from disparate clustering methods by building relationships between them. The built relationships are then represented as formal context using the Formal Concept Analysis (FCA) technique. COGA aims to generate ontology from clusters relationship built by CCA. By combining these two techniques, we are able to perform ontology learning from a citation database using clustering results. We have implemented the improved system and have demonstrated its use for finding research domain expertise. We have also conducted performance evaluation on the system and the results are encouraging.  相似文献   

Scientific community detection via bipartite scholar/journal graph co-clustering     

Chiara Carusi  Giuseppe Bianchi 《Journal of Informetrics》2019,13(1):354-386

This paper stems from the observation that researchers in different fields tend to publish in different journals. Such a relationship between researchers and journals is quantitatively exploited to identify scientific community clusters, by casting the community detection problem into a co-clustering problem on bipartite graphs. Such an approach has the potential of leading not only to the fine- grained detection of scholar communities based on the similarity of their research activity, but also to the clustering of scientific journals based on which are the most representative of each community. The proposed methodology is purely data-driven and completely unsupervised, and does not rely on any semantics (e.g. keywords or a-priori subjective categories). Moreover, unlike “flat” data structures (e.g. collaboration graphs or citation graphs) our bipartite graph approach blends in a joint structure both the researcher's attitude and interests (i.e., freedom to select the venue where to publish) as well as the community's recognition (i.e., acceptance of the publication on a target journal); as such may perhaps inspire further scientometric evaluation strategies. Our proposed approach is applied to the Italian research system, for two broad areas (ICT and Microbiology&Genetics), and reveals some questionable aspects and community overlaps in the current Italian scientific sectors classification.  相似文献   

基于查询意图聚类的实时搜索建议     

周之诚 《现代图书情报技术》2011,27(2):87-93

对于搜索引擎返回的结果太多且较少考虑用户个性差异等缺陷,提出根据用户查询意图,实时给予多个主题的搜索建议,帮助用户更准确地描述所需信息,修正查询词与真实意图之间的差距,提高检索效率。同时运用K-means算法,对资源类别的意图特征值相似用户进行聚类,缩小查找目标对象最近邻居的范围,提高搜索建议的实时响应速度。实验结果表明,该方法是可行的。  相似文献   

基于聚类算法的本体层次关系获取研究     

谷俊  朱紫阳 《现代图书情报技术》2011,(12):46-51

提出利用蚁群聚类方法进行初始聚类,通过K-means聚类算法对初始聚类的结果进一步分层聚类,并结合术语综合相似度计算的方式提取每个类的标签,从而完成术语层次关系的构建。最后抽取部分实验结果,由领域专家对其进行评价,并对结果进行分析。  相似文献   

汉语科技词系统研究进展     

朱礼军  张运良  徐硕  乔晓东 《数字图书馆论坛》2011,(3):34-38

汉语科技词系统是科技词汇知识深层次组织与应用服务工具的集成体系。文章首先概述了词系统的内涵,然后总结了三年来在词汇自动获取、词空间构建和词聚类以及词系统的复杂网络分析评价上所取得的技术突破,并展望了科技词系统下一步的重点研发方向。  相似文献   

一种引入内容特征的混合推荐方法     

陈勇 《科技广场》2011,(11):10-12

本文使用聚类算法将项目和用户进行分组,从而引入内容特征,再结合协同过滤方法,构造一种混合的推荐方法。实验结果表明,本文的推荐方法在较高稀疏度下优于一般的协同过滤算法。  相似文献   

基于改进的K-means算法的异常检测     

苏巴提  张晓 《人天科学研究》2011,(11):76-78

聚类算法通常用于数据的聚类。除此,它还可以用于异常数据的检测。首先介绍了基于划分的聚类算法K-means,然后给出改进算法I-K-means的算法描述,最后通过实例进行异常分析。  相似文献   

基于高斯混合模型的图像检索算法研究     

肖维 《人天科学研究》2011,10(4):48-50

通过研究聚类算法在图像处理上的应用,提出了一种基于高斯混合模型聚类的图像检索方法。该检索方法首先提取每幅图像的特征,并以特征值为数据集建立高斯混合模型,得到所有图像的高斯混合模型。再以所有图像的混合模型参数集作为数据集,用基于高斯混合模型的聚类算法进行聚类。最后输出检索例图所在的类,即得到检索结果。  相似文献   

图像分割的自适应K均值聚类算法研究     

吴颖斌 《运城学院学报》2013,(5):77-79

使用K均值聚类算法完成对图像的分割是一种常见的分割技术,针对传统的K均值聚类算法的不足,提出了使用直方图波形的有效波峰个数自适应确定K值的大小,实验结果表明,该方法简单、灵活、容易实现,并能取得较好的结果。  相似文献   

Nearest-biclusters collaborative filtering based on constant and coherent values     

Panagiotis Symeonidis  Alexandros Nanopoulos  Apostolos N. Papadopoulos  Yannis Manolopoulos 《Information Retrieval》2008,11(1):51-75

Collaborative Filtering (CF) Systems have been studied extensively for more than a decade to confront the “information overload” problem. Nearest-neighbor CF is based either on similarities between users or between items, to form a neighborhood of users or items, respectively. Recent research has tried to combine the two aforementioned approaches to improve effectiveness. Traditional clustering approaches (k-means or hierarchical clustering) has been also used to speed up the recommendation process. In this paper, we use biclustering to disclose this duality between users and items, by grouping them in both dimensions simultaneously. We propose a novel nearest-biclusters algorithm, which uses a new similarity measure that achieves partial matching of users’ preferences. We apply nearest-biclusters in combination with two different types of biclustering algorithms—Bimax and xMotif—for constant and coherent biclustering, respectively. Extensive performance evaluation results in three real-life data sets are provided, which show that the proposed method improves substantially the performance of the CF process.

Yannis ManolopoulosEmail:

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