Unsupervised Clonal Selection Clustering Algorithm Based on Immune Algorithm
Based on the detailed analysis of clonal selection algorithm,proposes unsupervised clone selection clustering algorithm.Which is adaptive data driven by adjusting its parameters,it carries on the classification of data operations as soon as possible,improves the premature convergence problem,improves the speed of data clustering.By using several artificial and real-life data sets,comparing the performance between unsupervised clonal selection clustering algorithm K-means algorithm.The experimental results show that,this algorithm solves the K-means algorithm needs several classes of K determined in advance,and the second best value stuck faults,the classification accuracy,and it is much better than traditional K-means classification algorithm in function and with higher reliability.
NETL
NSTL
万方数据
维普
