A outlier detecting method based on high dimensional data space is advanced from the projected clustering algorithm. It is important to detect outliers in many data mining applications.
首先,运用一种映射聚类的算法寻找数据点相对密集的子空间。
参考来源 - 基于高维空间的聚类技术研究·2,447,543篇论文数据，部分数据来源于NoteExpress
To address the deficiencies of most existing gene clustering algorithms, a novel gene projected clustering algorithm is proposed.
针对现有基因投影聚类算法的不足，提出一种有效的基因投影聚类算法。
Secondly, a new projection index based on dynamic cluster rule is constructed in the PPDC model, which would finish the sample clustering based on the projected characteristic value.
以投影寻踪理论为基础，利用动态聚类方法构建投影指标，建立了水资源评价的投影寻踪动态聚类模型。
Secondly, a new projection index based on dynamic cluster rule is constructed in the PPDC model, which would finish the sample clustering based on the projected characteristic value.
在一个简单投影指标下，用新的优化途径建立了多元数据分类模型，并将其用于多指标的标准水质分类。
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