KNN based machine learning approach for text and document mining

被引：0

作者：

Bijalwan, Vishwanath ^{[1
]}

Kumar, Vinay ^{[2
]}

Kumari, Pinki ^{[3
]}

Pascual, Jordan ^{[4
]}

机构：

[1] Institute of technology Gopeshwar, Chamoli, Uttarakhand

[2] GLA University, Mathura

[3] Bansathali University, Rajasthan

[4] Department of Computer Science, University of Oviedo

来源：

International Journal of Database Theory and Application | 2014年 / 7卷 / 01期

关键词：

Document mining; Event models; KNN; Machine learning; Naïve bayes; Term-graph; Text mining;

D O I：

10.14257/ijdta.2014.7.1.06

中图分类号：

学科分类号：

摘要：

Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much attention in the last years from both researchers in the academia and industry developers. In this paper, we first categorize the documents using KNN based machine learning approach and then return the most relevant documents. © 2014 SERSC.

引用

页码：61 / 70

页数：9

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