Experimental study of Data clustering using k-Means and modified algorithms

M.P.S, Bhatia; Khurana, Deepika

doi:10.5121/ijdkp.2013.3302

Cited by 20 publications

(1 citation statement)

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“…(2) Set a starting point for each cluster as the initial estimate for each cluster seed (Sharma et al, 2012). (3) Calculate the distance between each piece of information and cluster centre, and distribute data to the cluster with the shortest distance (Bhatia & Khurana, 2013). (4) Re-calculate the distance to the new centre (Ghosh & Dubey, 2013).…”

Section: K-means Clusteringmentioning

confidence: 99%

The Categorising Characteristics of Facebook Pages: Using the K-Means Grouping Method

Luarn

Lin

Chiu

et al. 2016

IJBM

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This study conducts the K-means grouping analysis on 1,373 Facebook pages in order to find the difference and characteristics between groups, and furthermore attempt to understand the behavioural characteristics of Facebook page users. The study produces four clusters with different characteristics, all of which are named and defined according to their qualities. The four types of pages are the "functional video and audio informational pages," "audio and video entertainment with low discussion pages," "high-identifying celebrity pages", and "food and travel with active discussion pages." The result of this study not only provides a clear understanding of the grouping structure of Facebook pages, but also provides reference for fan page managers when constructing management strategies.

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Section: K-means Clusteringmentioning

confidence: 99%