Author : Xinchun Yang
Affiliation : 1Department of Computer Engineering, Centrale Supélec, Pari
Country : France
Category : Computer Science & Information Technology
Volume, Issue, Month, Year : 8, 15, February, 2018
This paper concludes and analyses four widely-used algorithms in the field of online clustering: sequential K-means, basic sequential algorithmic scheme, online inverse weighted K-means and online K-harmonic means. All algorithms are applied to the same set of self-generated data in 2-dimension plane with and without noise separately. The performance of different algorithms is compared by means of velocity, accuracy, purity, and robustness. Results show that the basic sequential K-means online performs better on data without noise, and the K-harmonic means online performs is the best choice when noise interferes with the data.
Keyword : Sequential Clustering, online clustering, K-means, time-series clustering
For More Details : https://airccj.org/CSCP/vol8/csit89501.pdf