An Online Hot Topics Detection Approach Using the Improved Ant Colony Text Clustering Algorithm

Recently, with an increasing number of major events spreading all over the Internet, the research for online hot topics detection system has been paid more and more attention. In this paper, we proposed an unsupervised and efficient hot topics detection approach, which is based on an improved ant colony text clustering (IACTC) algorithm. In view of the deficiencies of the basic ant colony text clustering (BACTC) method, the inverse tangent function is introduced into the proposed algorithm as a novel probability function, which could be flexibly adjusted. Meanwhile, in order to reduce ants’ blinding movement, we add the memory organ to each ant, and we develop the concept of adaptive moving range. Additionally, we design a practical system framework with technical details to solve the problems involving in the real-time hot topics detection system (e.g., information collecting, web preprocessing and text processing). This system is also used to evaluate our proposed method .The experimental results show that our approach has numerous advantages and achieves satisfying results.