基于多种群协同微粒群优化的流数据聚类算法

针对流数据的实时、有序和维数高等特点, 提出一种基于多种群协同微粒群优化的流数据聚类算法. 该算法利用变量分而治之的思想, 多个种群协同优化多个类中心, 进而求出问题完整的类中心集合. 给出一种类中心变化趋势的预估策略, 以快速追踪环境变化. 为防止多个子微粒群同时优化一个类中心, 提出一种相似子微粒群的合并策略. 最后将所提出的算法用于多个数据集, 实验结果验证了算法的有效性.

Streaming data clustering using cooperative particle swarm optimization

Focusing on the stream data real time performance, orderliness, and high dimension, a streaming data clustering algorithm based on cooperative particle swarm optimization is proposed, which divides sequential stream data into several data subsets according to the time stamp. For any data subset, the high-dimensional clustering problem is firstly transformed into the low dimensional sub-problem with only one class center. Then, one sub-swarm optimizes one clustering sub-problem independently, and all the sub-swarms cooperate with each other to find the whole solution of the streaming data. Moreover, in order to enhance the speed of tracking the environment changes, a forecast strategy is designed to predict the change trend of class centers. In order to avoid multiple sub-swarms repeatedly searching for the same class center, a merging strategy of similar sub-swarms is proposed. Finally, the proposed algorithm is applied to multiple data sets, and experimental results show the effectiveness.