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To establish that this quantity is symmetric up to a logarithmic factor (I𝐾(𝑋;𝑌)≈I𝐾(𝑌;𝑋)) one requires the chain rule for Kolmogorov complexity.Approximations of this quantity via compression can be used to define a distance measure to perform a hierarchical clustering of sequences without having any domain knowledge of the sequences.

To establish that this quantity is symmetric up to a logarithmic factor (I𝐾(𝑋;𝑌)≈I𝐾(𝑌;𝑋)) one requires the chain rule for Kolmogorov complexity.Approximations of this quantity via compression can be used to define a distance measure to perform a hierarchical clustering of sequences without having any domain knowledge of the sequences.

为了确定这个量在对数因子<math>\operatorname{I}_K(X;Y) \approx \operatorname{I}_K(Y;X)</math>是对称的，需要'''<font color="#ff8000">Kolmogorov复杂性的链式规则 Chain rule for Kolmogorov complexity</font>'''。通过压缩对这个量的近似值可以用来定义'''<font color="#ff8000">距离度量 Distance measure</font>'''来执行序列的'''<font color="#ff8000">层次聚类 Hierarchical clustering</font>''，而不需要序列的任何领域知识。

为了确定这个量在对数因子<math>\operatorname{I}_K(X;Y) \approx \operatorname{I}_K(Y;X)</math>是对称的，需要'''<font color="#ff8000"> Kolmogorov复杂性的链式规则 Chain rule for Kolmogorov complexity</font>'''。通过压缩对这个量的近似值可以用来定义'''<font color="#ff8000">距离度量 Distance measure</font>'''来执行序列的'''<font color="#ff8000">层次聚类 Hierarchical clustering</font>'''，而不需要序列的任何领域知识。

=== 线性相关 Linear correlation ===

=== 线性相关 Linear correlation ===