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A Method for Characteristic Extraction from Large Sample Data Based on the Golden Section Method’s ISODATA Algorithm
Pages: 93-96
Year: Issue:  1
Journal: Journal of Inner Mongolia University(Natural Science Edition)

Keyword:  ISODATA 大样本 黄金分割法 特征提取;
Abstract: 如何从样本量大、数据结构复杂、离散度大的样本数据中提取有效的特征数据是模式识别的重点和难点,而ISODATA算法是处理大样本数据聚类的常用算法之一,其不足之处是需要预先确定初始聚类参数.提出了基于黄金分割法来度量聚类的有效性,该方法能动态计算聚类度量参数,可实现大样本数据的有效聚类.实验证明,该方法能够从原始特征中挑选出最有代表性、分类性能最好的特征.
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