A dynamic K-means clustering for data mining
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. Th...
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| Main Authors: | , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2019-02-01.
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| Online Access: | Get fulltext |
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| LEADER | 02774 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | ijeecs16674_10470 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Hossain, Md. Zakir |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Akhtar, Md.Nasim |e author |
| 700 | 1 | 0 | |a Ahmad, R.B. |e author |
| 700 | 1 | 0 | |a Rahman, Mostafijur |e author |
| 245 | 0 | 0 | |a A dynamic K-means clustering for data mining |
| 260 | |b Institute of Advanced Engineering and Science, |c 2019-02-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16674 | ||
| 520 | |a 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. | ||
| 540 | |a Copyright (c) 2018 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Clustering, Centroid, Data mining, Euclidean distance, K-Means, Threshold value. | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 13, No 2: February 2019; 521-526 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v13.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16674/10470 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/16674/10470 |z Get fulltext |