新含义的相似性

M. K. Nasution, Opim Salim Sitompul, M. Elveny, Rahmad Syah, Romi Fadillah Rahmat
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引用次数: 3

摘要

抽取是从信息空间(如社交网络)中获取知识的一种方式。表达与意义相关的知识的一个有效而简洁的工具是涉及相似性的概念。有几个相似的公式,从对象接近相同的东西,但有不同的任务,根据他们的功能。然而,测量结果揭示了不同的含义,尽管它们仍然处于相互支持的位置。因此,本文旨在通过相似性公式中除数之间的差异来证明相似函数的不同含义,并以此来表达相似函数的新含义。不同的相似性度量产生不同的和新的意义,支持模拟和聚类来管理大数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A similarity for new meanings
Extraction is a way to get knowledge from information space, such as social networks. One efficient and concise tool for expressing knowledge related to meaning is to involve the concept of similarity. There are several similarity formulations to approach the same thing from the objects but have different tasks according to their functions. However, the measurement results reveal different meanings, although they remain in a mutually supportive position. Therefore, this paper aims to express the different meanings besides new meanings of the similarity function, which are proven based on the difference between the divisors of the similarity formulation. Different measurements of similarities produce different and new meanings with supporting simulation and clustering to manage big data.
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