The subject of this study is three fundamental procedures of contemporary data analysis: outlier detection, clustering and classification. These issues are considered in a conditional approach – the introduction of specific (e.g., current) values to the model allows, in practice, a significantly precise description of the reality under research. The same methodology has been used for all three of the above tasks, and it considerably facilitates the interpretations, potential modifications and practical applications of the material investigated. Using non-parametric methods frees the procedures under investigation from a distribution in the considered dataset. This paper contains a complete set of formulas that allow easy implementation of the presented material in real-world problems.

本研究的主题是当代数据分析的三个基本过程：异常值检测，聚类和分类。在有条件的方法中考虑了这些问题–在模型中引入特定（例如当前）值实际上可以对研究中的现实进行非常精确的描述。上述所有三个任务都使用了相同的方法，并且极大地方便了所研究材料的解释，潜在的修改和实际应用。使用非参数方法可将正在研究的程序从考虑的数据集中的分布中解放出来。本文包含一套完整的公式，可以轻松实现实际问题中所介绍的材料。