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To establish that this quantity is symmetric up to a logarithmic factor (<math>\operatorname{I}_K(X;Y) \approx \operatorname{I}_K(Y;X)</math>) one requires the [[chain rule for Kolmogorov complexity]] {{Harvard citation|Li|Vitányi|1997}}. Approximations of this quantity via [[Data compression|compression]] can be used to define a [[Metric (mathematics)|distance measure]] to perform a [[hierarchical clustering]] of sequences without having any [[domain knowledge]] of the sequences {{Harvard citation|Cilibrasi|Vitányi|2005}}.

To establish that this quantity is symmetric up to a logarithmic factor (<math>\operatorname{I}_K(X;Y) \approx \operatorname{I}_K(Y;X)</math>) one requires the [[chain rule for Kolmogorov complexity]] {{Harvard citation|Li|Vitányi|1997}}. Approximations of this quantity via [[Data compression|compression]] can be used to define a [[Metric (mathematics)|distance measure]] to perform a [[hierarchical clustering]] of sequences without having any [[domain knowledge]] of the sequences {{Harvard citation|Cilibrasi|Vitányi|2005}}.

To establish that this quantity is symmetric up to a logarithmic factor (<math>\operatorname{I}_K(X;Y) \approx \operatorname{I}_K(Y;X)</math>) 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.

为了证明这个量对称于一个对数因子(math operatorname { i } k (x; y) approx operatorname { i } k (y; x) / math) ，我们需要一个柯氏复杂性的链式规则。通过压缩这个量的近似值可以用来定义一个距离度量来执行一个层次化的序列聚类，而不需要任何关于序列的领域知识。

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 ===