Author(s)

Zhenpeng Liu^1,2, Jing Dong¹, Bin Zhang^2*, Mengjie He¹, Jianmin Xu³

¹School of Electronic Information Engineering, Hebei University, Baoding, China.
²Center for Information Technology, Hebei University, Baoding, China.
³School of Cyber Security and Computer, Hebei University, Baoding, China.

The existing fuzzy clustering algorithm (FCM) is sensitive to the initial center point. And simple clustering of distance can neither discovery hot topics on the Network accurately nor solve the problem of semantic diversity in Chinese. Aiming at these problems, an improved fuzzy clustering method based on dynamic adaptive step firefly algorithm (FA) was proposed. The clustering center was optimized by improved FA, and the FCM was used to complete the final clustering. First, the step length was adjusted adaptively in the current iteration, and the relationship between fireflies was established according to text similarity, then the topic influence value was applied to fuzzy clustering algorithm to improve fitness function optimization. In this process the topic was categorized into the closest class to the cluster center, which can reduce the impact of topic variation. Finally, according to the level of influence value got hot topics. By collecting real data from Sina micro-blog, the effectiveness of the algorithm was verified by experiments, and the accuracy of topic discovery was improved greatly.

Topic Discovery, Firefly Algorithm, Dynamic Adaptive Step Size, FCM, Micro-Blog

Liu, Z. , Dong, J. , Zhang, B. , He, M. and Xu, J. (2018) Network Hot Topic Discovery of Fuzzy Clustering Based on Improved Firefly Algorithm. Journal of Computer and Communications, 6, 1-14. doi: 10.4236/jcc.2018.68001.