Compute the CDBW validity index
cdbw的Python项目详细描述
CDBW
计算s-dbw有效性指数
s_dbw有效性指数由公式定义：
cdbw=密实度*凝聚力*分离度
最高值->；更好的群集。
安装：
pip install --upgrade cdbw
用法：
fromcdbwimportCDbwscore=CDbw(X,labels,metric="euclidean",alg_noise='comb',intra_dens_inf=False,s=3,multipliers=False)
参数：
X : array-like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row corresponds
to a single data point.
labels : array-like, shape (n_samples,)
Predicted labels for each sample. (-1 - for noise)
metric : str,
The distance metric, can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’,
‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’,
‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’,
‘yule’.
alg_noise : str,
Algorithm for recording noise points.
'comb' - combining all noise points into one cluster (default)
'sep' - definition of each noise point as a separate cluster
'bind' - binding of each noise point to the cluster nearest from it
'filter' - filtering noise points
intra_dens_inf : bool,
If False (default) CDbw index = 0 for cohesion or compactness - inf or nan.
s : int,
Number of art representative points. (>2)
multipliers : bool,
Format of output. False (default) - only CDbw index, True - tuple (compactness, cohesion, separation, CDbw)
返回：
cdbw : float,
The resulting CDbw validity index.
参考文献：
- M.Halkidi和M.Vazirgiannis，“基于密度的多代表聚类有效性方法” 模式识别字母29（2008）773–786
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