# A density-based method for selection of the initial clustering centers of K-means algorithm

@article{Du2017ADM, title={A density-based method for selection of the initial clustering centers of K-means algorithm}, author={Xin Du and N. Xu and Cailan Zhou and Shihui Xiao}, journal={2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)}, year={2017}, pages={2509-2512} }

The initial clustering centers of traditional K-means algorithm are randomly generated from a data set, clustering effect is not very stable. Aimed at this problem, this paper puts forward a kind of optimal selection of the initial clustering center of K-means algorithm based on density, by calculating the local density of each data point and the minimum distance between that point and any other point with higher local density, choose K points with higher local density as the initial clustering… Expand

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#### References

SHOWING 1-9 OF 9 REFERENCES

An Initialization Method for Clustering High-Dimensional Data

- Computer Science
- 2009 First International Workshop on Database Technology and Applications
- 2009

A local density based method to search for initial cluster centers on high-dimensional data by defining the probability density of a point as the amount of its highly similar neighborhoods with weight coefficient. Expand

An Improved Initialization Center Algorithm for K-Means Clustering

- Computer Science
- 2010 International Conference on Computational Intelligence and Software Engineering
- 2010

Experiments based on the standard database UCI show that the proposed method can produce a high purity clustering results and eliminate the sensitivity to the initial centers of k-means algorithm to some extent. Expand

K-means clustering algorithm based on optimal initial centers related to pattern distribution of samples in space

- Computer Science
- 2012

This paper proposed a new algorithm to find the optimal initial centers for K-means clustering algorithm that achieves excellent clustering result in short run time and is insensible to noisy data. Expand

A K-means Algorithm Based on Optimized Initial Center Points

- Computer Science
- 2009

Aiming at the problems of K-means algorithm, a method is proposed to optimize the initial center points through computing the density of objects and consequently the cluster number is generated automatically. Expand

An efficient k-means clustering algorithm

- Computer Science
- 1997

The experimental results demonstrate that the proposed scheme can improve the computational speed of the direct k-means algorithm by an order to two orders of magnitude in the total number of distance calculations and the overall time of computation. Expand

An optimized genetic K-means clustering algorithm

- Computer Science
- 2012 International Conference on Computer Science and Information Processing (CSIP)
- 2012

This paper proposes an optimized genetic K-means clustering algorithm based on genetic algorithm that uses encoding, initialization, fitness function selection, crossover and mutation of genetic algorithms into clustering problem. Expand

Density-Sensitive Spectral Clustering

- Mathematics
- 2007

Spectral clustering has become increasingly popular in recent years.Being a pairwise method,the success of spectral clustering depends heavily on the choice of similarity measure.Through analyzing… Expand

Some methods for classification and analysis of multivariate observations

- Mathematics
- 1967

The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give… Expand