This document discusses K-means clustering, an unsupervised machine learning algorithm. It begins with an introduction to clustering and describes K-means clustering as assigning data points to K number of centroids, or cluster centers. The document outlines the K-means clustering procedure, which iteratively assigns data points to the closest centroid and recomputes centroids until centroids do not change. Advantages include faster computation than hierarchical clustering for large datasets, while disadvantages include difficulty selecting the optimal K value. Applications include wireless sensor networks, city planning, search engines, and email filtering.

1. K-MEANS CLUSTERING
PRESENTED BY-
DEEPAK VERMA(14052140019)
AJAY(1405214003)

2. OVERVIEW
 INTRODUCTION
 TYPE OF CLUSTERING
 IMPLEMENTATION DETAILS
 FLOW CHART
 ADVANTAGE AND DISADVANTAGE OF
CLUSTERING
 APPLICATIONS
 REFERENCES

3. INTRODUCTION
 A Cluster is a group of similar objects.
 Clustering refers to a method by which large
sets of data are grouped into clusters of
smaller sets of similar data.
Let us consider an example:-
f the three different colors into three
Different groups.

4.  The balls of same color are clustered into a
group as shown below
Types of Clustering:-
 Hard clustering
 Soft clustering

5. Types of Clusters
Exclusive Clustering:
Data is grouped in an exclusive way, so that if a
certain datum belongs to a definite cluster then it
could not be included in another cluster.
E.g. K-means
OverlappingClustering:
The overlapping clustering, uses fuzzy sets to
cluster data, so that each point may belong to
two or more cluster with different degrees of
member ship.

6. Hierarchical clustering:
“A set of nested clusters organized as a
hierarchical tree”
•The hierarchical methods produce a set of
nested clusters in which each pair of objects
or clusters is progressively nested in a larger
cluster until only one cluster remains

7. Clustering Algorithms:-
 A clustering algorithm attempts to find natural
groups of component (or data)based on some
similarity.
 The clustering algorithm finds the centroid of
group of datasets.
 Most algorithms evaluate the distance between a
point and the cluster centroids
RAW DATA CLUSTERINGALGORITHM CLUSTERS OF DATA

8. K-Means Algorithm:-
 It is a distance-based, Partitional Clustering
algorithm.
 “K” stands for number of clusters, it is a user
input to the algorithm.
 It is unsupervised algorithm.
 Each cluster is associated with a centroid.
 Each point is assigned to cluster with closest
centroid.
 This algorithm is iterative in nature.

9. Procedure:-
1) Select K points as the initial centroid.
2) Repeat it again.
3) From K clusters by assigning all points to
the closest centroid.
4) Re-compute the centroid of each cluster.
5) Until the centroids don’t change

10. START
Number of
Cluster K
Centroid
Distance Objects of
Centroids
Grouping Based of
Minimum Distance
No
object
move
group
END
FLOW CHART:

11. Pick
k=3
Initial
Cluster
Centers
(rando
mly)
Y
STEP.1
k2
k1
k3
X

12. k2
k1
k3Y
Assign
each
point
To the
Closest
Cluster
center
X
STEP.2

13. k2
k1
k3
Y
X
Move
each
Cluster
Center
To the
Mean
Of each
cluster
STEP.3

14. k2
k1
k3
X
Y
Reassign
Points
Closest
to a
Different
New
Cluster
center
Q.Which
Points are
reassigned
STEP.4 Continue

15. k2
k1
k3
X
Y
A:three
Points
With
animation
STEP.4

16. k2
k1
k3
X
Y
Re-compute
Cluster
means
STEP.5

17. k2
k1
k3
X
Y
Move
Cluster
Centers
To cluster
means
STEP.6

18. ADVANTAGES
1. If variables are huge, then the K-Means most of the
times computationally faster than hierarchical clustering.
If we keep k smalls.
2. K-Means produce tighter clusters than hierarchical
clustering , especially if the cluster are globular.
DISADVANTAGES
1. Difficult to product k-value.
2. It does not work well with clusters of Different size and
Different density.

19. Applications
 Wireless sensor networks
 City-planning
 Search Engines
 Email Filtering