DASFA-FCM proceed as follows:
① Initialization parameters: γ, Tmax, m, generate initial population , n indicates all micro-blog texts, k represents the number of initial cluster centers, initializing the position of each firefly.
② Calculating the influence value A(Xi) of each firefly according to Formula (9).
③ Calculating similarity between two texts(comparison of each micro-blog text and class center). when , the value of β0, uij are 0; when , all are 1. In this moment, the mutual attraction between fireflies is calculated according to Formula (2).
④ According to Formula (7), calculating the dynamic adaptive step length under the current iteration.
⑤ Calculating fitness function F(Xi), F(Xj); if F(Xi) < F(Xj), it shows that the firefly i influence is bigger than j, firefly i is in a better position than j, so firefly j moves to i, update each firefly position according to Formula (8).
⑥ Repeating steps ③ to ⑤ until the maximum number of iteration is reached. We can get the center of the cluster with the most influential fireflies. The number of the cluster center is C.
⑦ Based on the initial class centers found above, calculating the cluster center and membership matrix.
⑧ Calculating the distance from the micro-blog text i to the cluster c, and classifying topics into the nearest cluster center.
⑨ Repeating steps ⑦ and steps ⑧. If the termination condition is reached, the location and influence of the most influential firefly will be output, and the result after clustering, otherwise continue.
⑩ We get hot topics based on the arrangement of influence values, output the top 50% topics.