基于Ward’s方法的k-平均优化算法及其应用

通过对k-平均算法存在不足的分析,提出了一种基于Ward’s方法的k-平均优化算法。算法首先在用Ward’s方法对样本数据初步聚类的基础上,确定合适的簇数目、初始聚类中心等k-平均算法的初始参数,并进行孤立点检测、删除;基于上述处理再采用传统k-平均算法进行聚类。将优化的k-平均算法应用到罪犯人格类型分析中,实验结果表明,该算法的效率、聚类效果均明显优于传统k-平均算法。

A Novel Feature Selection Framework for Automatic Web Page Classification

The number of Internet users and the number of web pages being added to www increase dramatically every day. It is therefore required to automatically and efficiently classify web pages into web directories. This helps the search engines to provide users with relevant and quick retrieval results. As web pages are represented by thousands of features, feature selection helps the web page classifiers to resolve this large scale dimensionality problem. This paper proposes a new feature selection method using Ward’s minimum variance measure. This measure is first used to identify clusters of redundant features in a web page. In each cluster, the best representative features are retained and the others are eliminated. Removing such redundant features helps in minimizing the resource utilization during classification. The proposed method of feature selection is compared with other common feature selection methods. Experiments done on a benchmark data set, namely WebKB show that the proposed method performs better than most of the other feature selection methods in terms of reducing the number of features and the classifier modeling time.

甘草化学成分快速定性分析与化学模式识别研究

目的:采用超高效液相色谱-串联四极杆飞行时间质谱(UPLC-Q-TOF-MSE)建立甘草化学成分的快速定性分析方法,并采用超高效相色谱(UPLC)指纹图谱与化学模式识别结合进而分析72批次甘草间化学成分的差异性,为甘草的质量控制与评价提供实验依据。方法:运用时间依赖型MSE扫描方式采集甘草样品的质谱数据,快速鉴定甘草的主要化学成分,并采用Ward’s Method对UPLC指纹图谱数据进行探索性数据分析,获取分类信息,最后采用对偶传播人工神经网络分析不同分类组别的化学成分差异。结果:共鉴定了57个化合物,Ward’s Method分析将72批甘草样本区分为4组,利用对偶传播人工神经网络获取了甘草不同组别间的显著性差异成分。结论:本方法可以快速、全面实现甘草化学成分的定性分析,比较甘草间的化学成分差异性,为甘草质量等级标准的建立提供了参考依据。