Towards a hybrid approach of primitive cognitive network process and K-means clustering for social network analysis

Chun Guan, Kevin Kam Fung Yuen*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

3 Citations (Scopus)

Abstract

Social network sites (SNSs) have been influencing the social activities of many people. Consequently, analysis of social network data may produce meaningful information for decision making. This paper represents the basic hybrid approach of Primitive Cognitive Network Process (PCNP) and classical K-Means Clustering for grouping users in social network sites (SNSs) into appropriate clusters by the similarities among users. This new method has combined the PCNP approach, which is a revised approach of the Analytic Hierarchy Process (AHP), and the K-means method for evaluating the weighted attributes influencing the similarity between users. The proposed approach can act as a friends referring function in various kinds of SNSs.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
Pages1267-1271
Number of pages5
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013 - Beijing, China
Duration: 20 Aug 201323 Aug 2013

Publication series

NameProceedings - 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013

Conference

Conference2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, GreenCom-iThings-CPSCom 2013
Country/TerritoryChina
CityBeijing
Period20/08/1323/08/13

Keywords

  • Clustering
  • Data mining
  • Decision making
  • K-Means
  • Primitive cognitive network process
  • Social network

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