CAJ | 학술논문

本文研究无线传感器网络数据的聚类分析问题.针对传统 k‐means 对初始聚类中心敏感和易于陷入局部次优解的缺点,提出一种基于传感器网络的分布式免疫遗传 k‐means 聚类算法.该算法将聚类中心作为染色体,通过遗传算法来优化传统 k‐means 聚类算法的初始聚类中心,将免疫算法的选择操作引入染色体的遗传进化中,使染色体的浓度和适应度共同对其在进化中被选择产生影响,实现了染色体种群的多样性保持机制和自我调节功能,将搜索工作引向全局最优,较好地解决了 k‐means 算法的早熟现象问题.实验结果证明,本文算法改进了数据的聚类划分效果,能够把聚类结果快速收敛至全局最优,聚类准确率较高.
본문연구무선전감기망락수거적취류분석문제.침대전통 k‐means 대초시취류중심민감화역우함입국부차우해적결점,제출일충기우전감기망락적분포식면역유전 k‐means 취류산법.해산법장취류중심작위염색체,통과유전산법래우화전통 k‐means 취류산법적초시취류중심,장면역산법적선택조작인입염색체적유전진화중,사염색체적농도화괄응도공동대기재진화중피선택산생영향,실현료염색체충군적다양성보지궤제화자아조절공능,장수색공작인향전국최우,교호지해결료 k‐means 산법적조숙현상문제.실험결과증명,본문산법개진료수거적취류화분효과,능구파취류결과쾌속수렴지전국최우,취류준학솔교고.
@@@@The problem of wireless sensor network data clustering analysis is researched . In order to solve the shortcomings that traditional k‐means is secsitive for the initial clustering center and easily fall into local optimal solution ,a distributed immune genetic algorithm k‐means based on sensor network is proposed .In the algorithm , cluster center has been as chromosomes ,and through the genetic algorithm to optimize the initial cluster centers of traditional k‐means clustering algorithm ,meanwhile the selecting operation of immune algorithm is introduced into the genetic evolution of chromosome ,causing the concentration and fitness of chromosome will impact whether the chromosome is selected in evolutionary ,which can realize chromosome population diversity maintaining mechanism and self regulating function , and the searching achieves the global optimum , so the algorithm can solve the prematurity problem of k‐means algorithm better . The experimental results show that , the algorithm has been improved data clustering effect ,and made the clustering can converge to the global optimal clustering rapidly ,and the clustering has higher accuracy .