DYNAMITE: Dynamic aggregation of mutually‐connected points based clustering algorithm for time series data
Abhimanyu Bhowmik, Madhushree Sannigrahi, Prithwish Guha, Deepraj Chowdhury, Sukhpal Singh Gill · Internet Technology Letters · 2022
Time‐series clustering typically entails clustering similar patterns across various time scales or comparing various point trajectories. However, this study emphasizes to group data points based on their motions and forecast how the clusters will evolve across a number of immediate time frames. To achieve this, we propose a DYNamic Aggregation of Mutually‐connected poInts clusTEring (DYNAMITE) based clustering algorithm for time series. DYNAMITE is based on the interaction between points in a time series and it majorly consists of three components: (1) cluster initialization; (2) calculation of mutually connected points; and (3) cluster updating.