基于SOM的聚类算法在抑郁症药物药效检测中的应用

Application of SOM-based Clustering Algorithm in Detecting Depression Drug's Efficacy

为了研究不同治疗抑郁症的药物(利血平、开郁安神胶囊)对抑郁症的治疗效果,用SOM和K-Means相结合的方法对用药后的抑郁症大鼠DNA的芯片样本进行聚类。结果表明,将K-Means用于收敛后的SOM网络,不仅较好地解决了SOM在训练样本后边界不清的问题,也将样本的训练结果映射在二维,有利于对聚类结果的呈现。此外,改进的算法能较好地解决K-Means对初始质心的选择难、对聚类类别的定量难等问题。

In order to judge different Depression Drug＇ s Efficacy （ Reserpine, Kai Yu An Shen Capsules）, this paper developed an algorithm based on combining SOM and K-Means together to cluster rat＇ s DNA Chip samples after depression rat taking certain medicine. Using K-Means after the convergence SOM, not only solved the problem of SOM＇ s unclear boundaries after training the samples, but also the training results finally mapped in the two-dimension. In addition, the improved algorithm can also solve the difficulties of K-Means＇ initial choice of the centroid, and the difficulties in offering cluster categories before cluster. Based on the experimental clustering of samples, reserpine seems better.