A survey: hybrid evolutionary algorithms for cluster analysis
Artificial Intelligence Review
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Fast, effective text clustering has been playing an important role in organizing web text information. Due to its self-organization, robustness, flexibility, and visualization, the ant-based clustering approach has been applied to text clustering. In this approach, however, the ant's moving is random, which leads to the convergence speed too slow. Aims at above mentioned problem, an ant-based fast text clustering approach (AFTC) is presented. This approach utilizes pheromone left by ants to avoid ant's moving randomicity, which can make the ant move towards direction which has high pheromone concentration a teach step, and the direction of moving is the orientation where the text vectors are relatively concentration. Compared with the K-means algorithm,the ant-based fast text clustering approach performs the self-organization clustering process without setting clustering center number in advance, which can achieve better performance. The experimental results illustrate that the average time of ant-based fast text clustering approach is faster than that of traditional ant-based text clustering.