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...

Full description

Saved in:
Bibliographic Details
Main Authors: Shi Na, Liu Xumin, Guan Yong
Format: Conference Proceeding
Language:English
Subjects:
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
Citations: Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
DOI:10.1109/IITSI.2010.74