A dynamic K-means clustering for data mining

Md. Zakir Hossain, Md Nasim Akhtar, R. Badlishah Ahmad, Mostafijur Rahman

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Data mining is the process of finding structure of data from large data sets. With this process, the decision makers can make a particular decision for further development of the real-world problems. Several data clusteringtechniques are used in data mining for finding a specific pattern of data. The K-means method isone of the familiar clustering techniques for clustering large data sets. The K-means clustering method partitions the data set based on the assumption that the number of clusters are fixed. The main problem of this method is that if the number of clusters is to be chosen small then there is a higher probability of adding dissimilar items into the same group. On the other hand, if the number of clusters is chosen to be high, then there is a higher chance of adding similar items in the different groups. In this paper, we address this issue by proposing a new K-Means clustering algorithm. The proposed method performs data clustering dynamically. The proposed method initially calculates a threshold value as a centroid of K-Means and based on this value the number of clusters are formed. At each iteration of K-Means, if the Euclidian distance between two points is less than or equal to the threshold value, then these two data points will be in the same group. Otherwise, the proposed method will create a new cluster with the dissimilar data point. The results show that the proposed method outperforms the original K-Means method.

Original languageEnglish
Pages (from-to)521-526
Number of pages6
JournalIndonesian Journal of Electrical Engineering and Computer Science
Volume13
Issue number2
DOIs
Publication statusPublished - Feb 1 2019

Fingerprint

K-means Clustering
Data mining
Data Mining
K-means
Number of Clusters
Clustering algorithms
Threshold Value
Large Data Sets
Clustering
Data Clustering
K-means Algorithm
Less than or equal to
Centroid
Clustering Methods
Clustering Algorithm
Partition
Iteration
Calculate

Keywords

  • Centroid
  • Clustering
  • Data mining
  • Euclidean distance
  • K-Means
  • Threshold value

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Control and Optimization
  • Electrical and Electronic Engineering

Cite this

A dynamic K-means clustering for data mining. / Hossain, Md. Zakir; Akhtar, Md Nasim; Ahmad, R. Badlishah; Rahman, Mostafijur.

In: Indonesian Journal of Electrical Engineering and Computer Science, Vol. 13, No. 2, 01.02.2019, p. 521-526.

Research output: Contribution to journalArticle

Hossain, Md. Zakir ; Akhtar, Md Nasim ; Ahmad, R. Badlishah ; Rahman, Mostafijur. / A dynamic K-means clustering for data mining. In: Indonesian Journal of Electrical Engineering and Computer Science. 2019 ; Vol. 13, No. 2. pp. 521-526.
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