Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm
Clustering analysis method is one of the main analytical methods in data mining, the method of clustering algorithm will influence the clustering results directly. This paper discusses the standard k-means clustering algorithm and analyzes the shortcomings of standard k-means algorithm, such as the...
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| Published in: | 2010 Third International Symposium on Intelligent Information Technology and Security Informatics pp. 63 - 67 |
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| Main Authors: | , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
01.04.2010
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| Subjects: | |
| ISBN: | 9781424467303, 1424467306 |
| Online Access: | Get full text |
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| Abstract | Clustering analysis method is one of the main analytical methods in data mining, the method of clustering algorithm will influence the clustering results directly. This paper discusses the standard k-means clustering algorithm and analyzes the shortcomings of standard k-means algorithm, such as the k-means clustering algorithm has to calculate the distance between each data object and all cluster centers in each iteration, which makes the efficiency of clustering is not high. This paper proposes an improved k-means algorithm in order to solve this question, requiring a simple data structure to store some information in every iteration, which is to be used in the next interation. The improved method avoids computing the distance of each data object to the cluster centers repeatly, saving the running time. Experimental results show that the improved method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the k-means. |
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| AbstractList | Clustering analysis method is one of the main analytical methods in data mining, the method of clustering algorithm will influence the clustering results directly. This paper discusses the standard k-means clustering algorithm and analyzes the shortcomings of standard k-means algorithm, such as the k-means clustering algorithm has to calculate the distance between each data object and all cluster centers in each iteration, which makes the efficiency of clustering is not high. This paper proposes an improved k-means algorithm in order to solve this question, requiring a simple data structure to store some information in every iteration, which is to be used in the next interation. The improved method avoids computing the distance of each data object to the cluster centers repeatly, saving the running time. Experimental results show that the improved method can effectively improve the speed of clustering and accuracy, reducing the computational complexity of the k-means. |
| Author | Liu Xumin Guan Yong Shi Na |
| Author_xml | – sequence: 1 surname: Shi Na fullname: Shi Na email: shina8237140@126.com organization: Coll. of Inf. Eng., Capital Normal Univ. CNU, Beijing, China – sequence: 2 surname: Liu Xumin fullname: Liu Xumin email: hellosn@126.com organization: Coll. of Inf. Eng., Capital Normal Univ. CNU, Beijing, China – sequence: 3 surname: Guan Yong fullname: Guan Yong email: whwqd@126.com organization: Coll. of Inf. Eng., Capital Normal Univ. CNU, Beijing, China |
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| Snippet | Clustering analysis method is one of the main analytical methods in data mining, the method of clustering algorithm will influence the clustering results... |
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| SubjectTerms | Algorithm design and analysis Clustering algorithms clustering analysis Computational complexity Data engineering Data mining distance Educational institutions Information analysis Iterative algorithms k-means algorithm Machine learning Partitioning algorithms |
| Title | Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm |
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