Finding the Behavior Internet User Based On Log Classification Using K-Medoid Clustering
Author(s):
N.vijayalakshmi, J S.Ananda Kumar
Keywords:
Clustering, K-Medoid, Network, Log, Cyber-profiling
Abstract
The most important one in the management of a network is to know the characteristics of the network users. The Internet is necessity in today's society; any information is getting through internet via web browser. However, these activities can show more impact on users. Here we focus on the activities of Internet users based on the log data network. The data used in this study resulted from one-week observation from one of the universities in Yogyakarta. Data log network activity is one type of big data, so it is needed to use of data mining with K-Medoid algorithm as a solution to determine the behavior of Internet users. The K-Medoid algorithm used for clustering, which is based on the number of visitors. The numbers of visitors are divided into three, namely low with 1479 amount of data, medium with 126 amounts of data, and high with 33 amounts of data. Categorization is based on the access time and is based on website content that exists in the data. It is to compare the results by the K-Medoid clustering algorithm. The results of educational institution show that each of these clusters produces websites that are frequented by the sequence: website search, social media, news, and information. This study also revealed that the cyber-profiling had been done strongly influenced by environmental factors and daily activities.
Article Details
Unique Paper ID: 145498

Publication Volume & Issue: Volume 4, Issue 10

Page(s): 572 - 576
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