s-dbw 0.4.0

Description:

sdbw 0.4.0

S_Dbw
###Compute the S_Dbw or SD validity index
####S_Dbw validity index is defined by equation:
S_Dbw = Scatt + Dens_bw
where Scatt - means average scattering for clusters and Dens_bw - inter-cluster density.
Lower value -> better clustering.
####SD validity index is defined by equation:
SD = k*Scatt + distance
where distance - distances between cluster centers, k - weighting coefficient equal to distance(Cmax).
Lower value -> better clustering.
Installation
pip install --upgrade s-dbw

Usage
from s_dbw import S_Dbw
score = S_Dbw(X, labels, centers_id=None, method='Tong', alg_noise='bind',
centr='mean', nearest_centr=True, metric='euclidean')

#####OR
from s_dbw import SD
score = SD(X, labels, k=1.0, centers_id=None, alg_noise='bind',centr='mean', nearest_centr=True, metric='euclidean')

Parameters:

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).
centers_id : array-like, shape (n_samples,)
The center_id of each cluster's center. If None - cluster's center calculate automatically.
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
centr : str,
cluster center calculation method (mean (default) or median)
nearest_centr : bool,
The centroid corresponds to the cluster point closest to the geometric center (default: True).
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’.
Default is ‘euclidean’.
#####For S_Dbw:
method : str,
S_Dbw calc method:
'Halkidi' - original paper [1]
'Kim' - see [2]
'Tong' - see [3]
#####For SD:
k: float,
The weighting coefficient equal to distance(Cmax). It is necessary for evaluating solutions with vary number of clusters because distance(C) depends on number of clusters [4].

Returns
score : float
The resulting S_Dbw or SD score.
References:

M. Halkidi and M. Vazirgiannis, “Clustering validity assessment: Finding the optimal partitioning of a data set,” in ICDM, Washington, DC, USA, 2001, pp. 187–194.
Youngok Kim and Soowon Lee. A clustering validity assessment Index. PAKDD’2003, Seoul, Korea, April 30–May 2, 2003, LNAI 2637, 602–608
Tong, J. & Tan, H. J. Electron.(China) (2009) 26: 258. https://doi.org/10.1007/s11767-007-0151-8
Halkidi, Maria & Vazirgiannis, Michalis & Batistakis, Yannis. (2000). Quality Scheme Assessment in the Clustering Process. LNCS (LNAI). 1910. 265-276. 10.1007/3-540-45372-5_26.