大型TREC生物医学文档的层次化聚类框架

P. Kumari, M. Jeeva, C. Satyanarayana
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引用次数: 0

摘要

诸如生物医学、生物医学、缺陷或bug数据库等微博站点的增长使得web用户难以在文本聚类应用程序上共享和表达他们对顺序关键短语及其类别的上下文识别。在传统的文档分类和聚类模型中,与TREC文本相关的特征分析更为复杂。随着存储库大小的增加,在大量非结构化文档中查找相关的基于特性的关键短语模式变得越来越困难。本研究的目的是在大型TREC数据存储库上开发和实现一个新的分层文档聚类框架。采用文档特征选择和聚类模型从TREC生物医学临床基准数据集中识别和提取MeSH相关文档。实验结果表明,该模型在计算内存、准确性和错误率方面具有较高的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Hierarchical Document Clustering Framework on Large TREC Biomedical Documents
The growth of microblogging sites such as Biomedical, biomedical, defect, or bug databases makes it difficult for web users to share and express their context identification of sequential key phrases and their categories on text clustering applications. In the traditional document classification and clustering models, the features associated with TREC texts are more complex to analyze. Finding relevant feature-based key phrase patterns in the large collection of unstructured documents is becoming increasingly difficult, as the repository's size increases. The purpose of this study is to develop and implement a new hierarchical document clustering framework on a large TREC data repository. A document feature selection and clustered model are used to identify and extract MeSH related documents from TREC biomedical clinical benchmark datasets. Efficiencies of the proposed model are indicated in terms of computational memory, accuracy, and error rate, as demonstrated by experimental results.
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