Clustering algorithms group similar objects together by identifying commonalities between data points. There are several types of clustering algorithms, including connectivity-based hierarchical clustering which connects objects into clusters based on distance; centroid-based clustering which represents clusters by central vectors like k-means; distribution-based clustering which models clusters as belonging to the same statistical distribution; and density-based clustering which identifies clusters as dense regions separated by sparse areas. Clustering has applications across many domains including biology, market research, medicine, social science, and computer science.

1. Clustering algorithms
Presented by :
Issra’a Haider Hashim
Depatment :Electronic &
Communication Engineering

2. Outline:
1
What is clustering?
2
Clustering Algorithms
3
Applications

3. What is Clustering?
 Is grouping similar objects together.
 Is the task of grouping a set of objects in such a
way that objects in the same group (called
a cluster) are more similar (in some sense or
another) to each other than to those in other
groups (clusters) .
 Clustering can therefore be formulated as
a multi-objective optimization problem.

4. A BBB
Females Males

5. A BBB
BBB
A
SIMPSONS Family SIMPSONS neighbors
Children Adults

6. BBB
BBB
A
A
Tall People
With hair
Short People
Hairless

7. “You need to know the question you
are trying to answer “
- Jason Bell (2014)
Bell,2014,Machine Learning: Hands-on for developers and technical professionals

8. Clustering
Algorithms
Connectivity-based
(hierarchical clustering)
Centroid -
based
Distribution-
based
Density-
based
Other
Algorithms

9. •Connectivity-based clustering (hierarchical
clustering):
These algorithms connect "objects" to form "clusters"
based on their distance. At different distances,
different clusters will form, which can be represented
using a dendrogram (from Greek dendro "tree" and
gramma "drawing" is a tree diagram).
Names Formulas
Euclidean distance
Manhattan distance
Squared Euclidean distance
Maximum distance
Mahalanobis distance

11. • Centroid-based clustering
In centroid-based clustering, clusters are represented by a central vector, which
may not necessarily be a member of the data set. When the number of clusters
is fixed to k, k-means clustering gives a formal definition as an optimization
problem: find the k cluster centers and assign the objects to the nearest cluster
center, such that the squared distances from the cluster are minimized.
The optimization problem itself is known to be NP-hard, and thus the common
approach is to search only for approximate solutions. A particularly well known
approximate method is Lloyd's algorithm,[8] often just referred to as "k-means
algorithm" (although another algorithm introduced this name). It does however
only find a local optimum, and is commonly run multiple times with different
random initializations. Variations of k-means often include such optimizations as
choosing the best of multiple runs, but also restricting the centroids to members
of the data set (k-medoids), choosing medians (k-medians clustering), choosing
the initial centers less randomly (k-means++) or allowing a fuzzy cluster
assignment (fuzzy c-means).

13. • Distribution-based clustering
The clustering model most closely related to statistics
is based on distribution models. Clusters can then
easily be defined as objects belonging most likely to
the same distribution. A convenient property of this
approach is that this closely resembles the way
artificial data sets are generated: by sampling random
objects from a distribution.

14. • Density based clustering:
In density-based clustering , clusters are defined as
areas of higher density than the remainder of the data
set. Objects in these sparse areas - that are required to
separate clusters - are usually considered to be noise
and border points.

15. Applications
Biology
Market
research
Medicine Social Science
World Wide
Web
Others
Computer
science

16. Confused , Comment , or any questions