Xinchun Yang^1,2, Wassim Kabbara^1,3, ¹Centrale Supelec, France, ²Tsinghua University, China and ³Lebanese University, Lebanon

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.

Sequential Clustering, online clustering, K-means, time-series clustering