Cluster Evaluation of Online Social Network's Data by Using K-Means Algorithm

Autor:	Ajmer Singh, Nidhi Chawla
Rok vydání:	2014
Předmět:	Basis (linear algebra) Social network business.industry Process (engineering) media_common.quotation_subject k-means clustering computer.software_genre Disease cluster Identification (information) Quality (business) Data mining business Cluster analysis computer Simulation media_common
Zdroj:	International Journal of Data Mining and Emerging Technologies. 4:83-91
ISSN:	2249-3220 2249-3212
DOI:	10.5958/2249-3220.2014.00005.6
Popis:	Clustering is the statistical technique used for identification of how various units like people, groups or societies can be grouped together on the basis of their common characteristics. In recent days, communication and advertising through social networking sites are most popular and interactive strategy among the users. The significance of the proposed work is determined with the help of online surveys of those people who have used social network sites. This paper presents data clustering process by using K-means algorithm for identification of cluster of users with common online behaviour. Furthermore by using WEKA data mining tool we have evaluated the performance of cluster on the basis of quality, accuracy and efficiency on two different cluster modes.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::10516c1201f34ce0f1bb0eed2a0e5abb https://doi.org/10.5958/2249-3220.2014.00005.6 Zobrazit plný text záznamu