Text Mining: Finding Hot Topics TF*PDF vs. LSI

被引：0
|
作者：
Katyayani, J. [1 ]
Sriharsha, A. V. [2 ]
Sudhir, B. [3 ]
机构：
[1] Sri Padmavathi Mahila Visva Vidyalayam, Tirupati, Andhra Pradesh, India
[2] Sree Vidyanikethan Engn Coll, Tirupati, Andhra Pradesh, India
[3] Sri Venkateswara Univ, Tirupati, Andhra Pradesh, India
来源：
2009 IEEE INTERNATIONAL WORKSHOP ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATIONS | 2009年
关键词：
Text mining; dimensionality reduction; latent-semantic indexing; IR;
D O I：
10.1109/IDAACS.2009.5342925
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
With the vast amount of digital text materials available on the Net, it is almost impractical for people to absorb all related information in a timely manner. This problem has been overcome by erstwhile researchers and scientists of data mining. The efficiency in the methods and exploratory analysis has to be ascertained yet. Document wise term frequencies and inverted frequencies are available to calculate the statistical importance among the documents. Determining the time line importance of the documents plays very essential role than just finding the document's importance. LSI is a basic PCA approach, which is proposed with time-line approach and has been discussed comparatively in this paper.
引用
收藏
页码：526 / +
页数：2
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