Thursday, September 17, 2020


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

Abstract :

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

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