关键词: DBSCAN算法, 人工蜂群优化算法, 截断-锦标赛选择机制, 自适应步长策略, 聚类

Abstract: Aiming at unreasonable global parameter settings, the difficulty of parameter selection and the impossibility of identifying overlapping modules based on density clustering algorithm DBSCAN, to overcome the disadvantage of slow convergence and being vulnerable to trap in local optima of artificial bees colony algorithm, this paper proposes a modified density-based clustering method based on the artificial bee colony optimization, named IABC-DBSCAN. First, a truncation-championship selection mechanism is proposed by combining the truncation selection mechanism and the tournament selection mechanism to enhance the diversity of the population and avoid falling into the local optimum when following bees search nectar source. Second, adaptive step strategy is designed to dynamically adjust the search behaviors of following bees, which strengthens the ability of finding the local solution of following bees and improves the clustering speed. Finally, the improved artificial bees colony is used to dynamic adjust DBSCAN’s optimal parameter, the nectar source of ABC algorithm is corresponding to the specified parameter [ε], where [ε] represents the maximum radius of a neighborhood, the income level of nectar stands for the performance of clustering. It is run on a number of test functions and data sets, which verifies the proposed algorithm. The experimental results show that the new algorithm not only overcomes the shortcoming of two original algorithm, but also the harmonic mean value of precision and recall gets greatly improved.

Key words: Density-Based Spatial Clustering of Application with Noise（DBSCAN） algorithm, artificial bee colony optimization, truncation-championship selection mechanism, adaptive step strategy, clustering