文件的并行比较器

Sonia Alouane-Ksouri, Minyar Sassi Hidri, Kamel Barkaoui
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引用次数: 5

摘要

文档聚类、句子聚类和词聚类是研究得很好的问题。大多数现有算法将文档、句子和单词分开聚类,而不是同时聚类。然而,在分析大型文本语料库时,要在一台机器中处理的数据量通常受到可用主内存的限制,并且要分析的这些数据的增加会导致计算工作负载的增加。在本文中,我们提出了一种并行模糊三合一相似度量,称为PFT-Sim,以计算基于并行编程架构的文档共聚类背景下的模糊隶属度。它允许同时计算文档/句子和句子/单词之间的模糊共相似矩阵。每一个都是建立在其他基础上的。PFT-SIM模型提供了一种并行数据分析策略,并将相似度计算任务划分为并行子任务,以解决效率和可扩展性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Parallel Comparator of Documents
Documents, sentences and words clustering are well studied problems. Most existing algorithms cluster documents, sentences and words separately but not simultaneously. However, when analyzing large textual corpuses, the amount of data to be processed in a single machine is usually limited by the main memory available, and the increase of these data to be analyzed leads to increasing computational workload. In this paper we present a parallel fuzzy triadic similarity measure called PFT-Sim, to calculate fuzzy memberships in a context of document co-clustering based on a parallel programming architecture. It allows computing simultaneously fuzzy co-similarity matrices between documents/sentences and sentences/words. Each one is built on the basis of the others. The PFT-SIM model provides a parallel data analysis strategy and divides the similarity computing task into parallel sub-tasks to tackle efficiency and scalability problems.
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