Scikit
- learn have
sklearn.cluster.DBSCAN module to perform DBSCAN clustering. There
are two important parameters namely
min_samples and
eps used by this algorithm to
define
dense . Higher value of parameter
min_samples or lower value of the parameter
eps will give
an indication about the higher density of data points which is necessary to form a cluster.
OPTICS It stands for “
Ordering points to identify the clustering structure ”. This algorithm
also finds density-based clusters in spatial data. It’s basic working logic is like DBSCAN.
It addresses a major weakness of DBSCAN algorithm-the problem of detecting meaningful
clusters in data of varying density-by ordering the points of the database in such a way
that spatially closest points become neighbors in the ordering.
Scikit
- learn have
sklearn.cluster.OPTICS module to perform OPTICS clustering.
BIRCH It stands for Balanced iterative reducing and clustering using hierarchies. It is used to
perform hierarchical clustering over large data sets. It builds a tree named
CFT i.e.
Characteristics Feature Tree , for the given data.
The advantage of CFT is that the data nodes called CF (Characteristics Feature) nodes
holds the necessary information for clustering which further prevents the need to hold the
entire input data in memory.
Scikit-Learn
133
Scikit
- learn have
sklearn.cluster.Birch module to perform BIRCH clustering.
Comparing Clustering Algorithms Following table will give a comparison (based on parameters, scalability and metric) of the
clustering algorithms in scikit-learn.
