关键词: K-Means算法, 文本聚类, 灰狼优化算法, 免疫克隆, 粒子群

Abstract:

Focusing the issue of K-Means algorithm is easy to fall into the local optimum during the text clustering process, which results in inaccurate text clustering results. The K-Means text clustering method based on the improved gray wolf optimization algorithm is proposed. After word segmentation, de-stopping, feature extraction, and text vectorization of text data, the elite individuals are selected through immune cloning, and the elite individuals are explored in depth to increase the diversity of the gray wolf population and avoid premature convergence. It combines the particle swarm location update idea with the gray wolf location update to reduce the risk of the gray wolf optimization algorithm falling into local extremes. Finally, improved gray wolf optimization algorithm is combined with the K-Means algorithm for text clustering. Compared with the K-Means algorithm, GWO-KMeans and IPSK-Means algorithm, the proposed algorithm has significantly improved accuracy, recall and F-value average, respectively, the text clustering result is more reliable.

Key words: K-Means algorithm, text clustering, gray wolf optimization, immune clone, particle swarm