Analysis and Implementation of Graph Clustering for Digital News Using Star Clustering Algorithm

International Conference on Computing and Applied Informatics 2016

物理学;计算机科学

Ahdi, A.B.^1 ; Sw, K.R.^1 ; Herdiani, A.^1

Jl. Telekomunikasi, Bandung, West Java

40287, Indonesia^1

关键词: Appropriate models;    Cluster qualities;    Digital news;    Expert judgement;    Graph clustering;    Graph clustering algorithms;    Graph model;    High-accuracy;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/801/1/012061/pdf
DOI  :  10.1088/1742-6596/801/1/012061

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Since Web 2.0 notion emerged and is used extensively by many services in the Internet, we see an unprecedented proliferation of digital news. Those digital news is very rich in term of content and link to other news/sources but lack of category information. This make the user could not easily identify or grouping all the news that they read into set of groups. Naturally, digital news are linked data because every digital new has relation/connection with other digital news/resources. The most appropriate model for linked data is graph model. Graph model is suitable for this purpose due its flexibility in describing relation and its easy-to-understand visualization. To handle the grouping issue, we use graph clustering approach. There are many graph clustering algorithm available, such as MST Clustering, Chameleon, Makarov Clustering and Star Clustering. From all of these options, we choose Star Clustering because this algorithm is more easy-to-understand, more accurate, efficient and guarantee the quality of clusters results. In this research, we investigate the accuracy of the cluster results by comparing it with expert judgement. We got quite high accuracy level, which is 80.98% and for the cluster quality, we got promising result which is 62.87%.