多属性单一趋势结构时序数据的聚类模型

摘要

According to the characteristic of multidimensional time series data of unitary trending structure,a weighted immune genetic fuzzy C-means clustering model was proposed.To clarify the similarity weights,weighted optimization model was estab-lished and the model was solved by using the improved particle swarm optimization algorithm.To overcome the problem that tra-ditional fuzzy C-means algorithm is sensitive to initial center,the immune mechanism was introduced into the genetic framework to improve fuzzy C-means algorithm.The experimental results show that the weighted optimization model is reasonable and ef-fective and the solving method has higher convergence precision and speed.The clustering method has higher convergence preci-sion compared with other methods.%针对多属性单一趋势结构时序数据的特点，提出一种加权免疫遗传模糊 C 均值聚类方法。为确立相似度权值，建立权值优化模型，利用改进离子群算法对模型进行求解；针对传统模糊 C 均值初始中心敏感的问题，将免疫机理引入到遗传算法框架中，对模糊 C 均值进行改进。实例验证结果表明，权值优化模型是合理有效的，求解方法具有较高的收敛精度及速度，与其它方法相比，聚类方法具有较高的收敛精度。