# scipy.cluster.hierarchy.maxinconsts¶

scipy.cluster.hierarchy.maxinconsts(Z, R)[source]

Return the maximum inconsistency coefficient for each non-singleton cluster and its children.

Parameters: Z : ndarray The hierarchical clustering encoded as a matrix. See linkage for more information. R : ndarray The inconsistency matrix. MI : ndarray A monotonic (n-1)-sized numpy array of doubles.

linkage
for a description of what a linkage matrix is.
inconsistent
for the creation of a inconsistency matrix.

Examples

>>> from scipy.cluster.hierarchy import median, inconsistent, maxinconsts
>>> from scipy.spatial.distance import pdist


Given a data set X, we can apply a clustering method to obtain a linkage matrix Z. scipy.cluster.hierarchy.inconsistent can be also used to obtain the inconsistency matrix R associated to this clustering process:

>>> X = [[0, 0], [0, 1], [1, 0],
...      [0, 4], [0, 3], [1, 4],
...      [4, 0], [3, 0], [4, 1],
...      [4, 4], [3, 4], [4, 3]]

>>> Z = median(pdist(X))
>>> R = inconsistent(Z)
>>> Z
array([[ 0.        ,  1.        ,  1.        ,  2.        ],
[ 3.        ,  4.        ,  1.        ,  2.        ],
[ 9.        , 10.        ,  1.        ,  2.        ],
[ 6.        ,  7.        ,  1.        ,  2.        ],
[ 2.        , 12.        ,  1.11803399,  3.        ],
[ 5.        , 13.        ,  1.11803399,  3.        ],
[ 8.        , 15.        ,  1.11803399,  3.        ],
[11.        , 14.        ,  1.11803399,  3.        ],
[18.        , 19.        ,  3.        ,  6.        ],
[16.        , 17.        ,  3.5       ,  6.        ],
[20.        , 21.        ,  3.25      , 12.        ]])
>>> R
array([[1.        , 0.        , 1.        , 0.        ],
[1.        , 0.        , 1.        , 0.        ],
[1.        , 0.        , 1.        , 0.        ],
[1.        , 0.        , 1.        , 0.        ],
[1.05901699, 0.08346263, 2.        , 0.70710678],
[1.05901699, 0.08346263, 2.        , 0.70710678],
[1.05901699, 0.08346263, 2.        , 0.70710678],
[1.05901699, 0.08346263, 2.        , 0.70710678],
[1.74535599, 1.08655358, 3.        , 1.15470054],
[1.91202266, 1.37522872, 3.        , 1.15470054],
[3.25      , 0.25      , 3.        , 0.        ]])


Here scipy.cluster.hierarchy.maxinconsts can be used to compute the maximum value of the inconsistency statistic (the last column of R) for each non-singleton cluster and its children:

>>> maxinconsts(Z, R)
array([0.        , 0.        , 0.        , 0.        , 0.70710678,
0.70710678, 0.70710678, 0.70710678, 1.15470054, 1.15470054,
1.15470054])


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