Clustering:
Things you should know
I know many of you know the definition
of clustering but few of you also might don’t know. So basically, clustering is
a kind of grouping when we don’t have any idea how to make the groups and
on what basis? In data science term it is unsupervised learning.
First, you have to understand
the difference between types of clustering and types of clustering algorithms.
Many of you consider the types of clustering algorithms i.e. K-means,
Hierarchical as types of clustering but it’s not. The following are the types
of clustering:
Yes, you saw right these
are the main types of clustering. Now we will look for each type.
1. Partitional: Partitional
clustering is nothing but making subsets of data that will not overlap. In
simple words making a cluster of each data object. So in partitional clustering, the
number of clusters is always the number of observations.
2. Hierarchical: If
we make sub-clusters of the cluster then it would be called hierarchical clustering.
It is the set of nested clusters that are organized as a tree.
3. Exclusive:
When variables make clusters of itself then that clusters are called exclusive clusters. e.g. if the dataset contains 20 variables then it will form
20 clusters itself.
4. Overlapping: If
the objects of the dataset find in more than one cluster then that clusters
are considered as overlapping clusters. Overlapping clusters are also called as
Non-Exclusive clusters.
5. Fuzzy: Sometimes objects of
the dataset belongs to every cluster, that time those clusters are considered
as fuzzy clusters.
6. Complete:
When every object of the dataset is added in clusters then those clusters are
called complete clusters.
7. Partial:
When clusters fail to add every object of the dataset. Then these clusters
considered partial clusters.
Nice content..if explanation is presented with some example then it would be very clear and helpful to understand easily.
ReplyDeleteThank you