Description: ISODATA算法
算法步骤：
(1) 预置
a. 设定聚类分析控制参数：
lc=预期的类数，
lNc=初始聚类中心个数(可以不等于c)，
lθn =每一类中允许的最少模式数目(若少于此数不能单独成为一类)，
lθs =类内各分量分布的距离标准差上界(大于此数就分裂)，
lθD=两类中心间的最小距离下界(若小于此数，这两
-ISODATA algorithm algorithm steps: (1) preset settings a. Cluster analysis control parameters: lc = expected number of categories, lNc = initial number of cluster centers (which may not equal to c), lθn = each category of permit the minimum number of models (if less than that and should not become a separate category), lθs = category weight distribution of the standard deviation of the distance between the upper bound of (larger than this number on the split), lθD = two types of the minimum distance between centers of the lower bound of ( If the number is smaller than this, these two Platform: |
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ISODATA算法是一种基于统计模式识别的，非常经典的非监督学习动态聚类算法，有较强的实用性。ISODATA算法不仅可以通过调整样本所属类别完成样本的聚类分析，而且可以自动地进行类别的“合并”和“分裂”，从而得到类数比较合理的聚类结果。-ISODATA algorithm is based on statistical pattern recognition, unsupervised learning is the classic dynamic clustering algorithm, there is a strong practical. ISODATA algorithm can be completed by adjusting the sample of category clustering analysis of samples, and the categories can be automatically " merge" and " split" to get class numbers more reasonable clustering results. Platform: |
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