The K-Means with Mini Batch Algorithm for Topics Detection on Online News

被引：0
|
作者：
Fitriyani, Siti Rofiqoh [1 ]
Murfi, Hendri [1 ]
机构：
[1] Univ Indonesia, Dept Math, Depok, Indonesia
来源：
2016 4TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY (ICOICT) | 2016年
关键词：
topic detection; K-means algorithm; mini batch; online news;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Online media is the most important media for accessing a wide range of information, such as news. Nowadays, there are many news agencies publish digital news via online media. The popularity of the online news makes the increasing volume of available news. This leads to the necessity of automated methods for news analysis, i.e. topics detection. One of The topic detection methods is the K-means algorithm. However, this algorithm is slow for big datasets. Therefore, the mini batch approach is used to reduce the computational time. Our experiments show that the computational times of the mini batch approach is much faster for the comparable accuracies.
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页数：5
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