User (K-Means) for clustering in Data Mining with application

Authors

  • قتيبة نبيل نايف
  • محي الدين خلف ايوب

DOI:

https://doi.org/10.33095/jeas.v22i91.491

Keywords:

العناصر ، تنقيب البيانات ، العنقدة ، التعليم الالي ، الخوارزمية., object ,data mining, clustering ,machine learning ,algorithm object ,data mining, clustering ,machine learning ,algorithm

Abstract

 

 

  The great scientific progress has led to widespread Information as information accumulates in large databases is important in trying to revise and compile this vast amount of data and, where its purpose to extract hidden information or classified data under their relations with each other in order to take advantage of them for technical purposes.

      And work with data mining (DM) is appropriate in this area because of the importance of research in the (K-Means) algorithm for clustering data in fact applied with effect can be observed in variables by changing the sample size (n) and the number of clusters (K) and their impact on the process of clustering in the algorithm.

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Published

2016-08-01

Issue

Section

Statistical Researches

How to Cite

“User (K-Means) for clustering in Data Mining with application” (2016) Journal of Economics and Administrative Sciences, 22(91), p. 389. doi:10.33095/jeas.v22i91.491.

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