International Journal of Trend in Scientific Research and Development (IJTSRD)
International Open Access Journal | www.ijtsrd.com
ISSN No: 2456 - 6470 | Volume - 2 | Issue – 6 | Sep – Oct 2018
Performance Analysis of K
K-mean
mean Clustering Map for Different Nodes
Subham Bhawsar, Aastha Hajari
Embedded
edded System and VLSI Design
Design, Department of Electronics and
nd Communication Engineering
Shiv Kumar Singh Institute of Technology & Science (SKSITS), Indore, Madhya Pradesh,
Pradesh India
ABSTRACT
WSNs is a group of inexpensive and autonomous
sensor nodes interconnected and work together to
monitor various environmental conditions such as
humidity, temperature, pressure, and other climate
changes. Wireless sensor nodes are randomly installed
and communicate themselves through the wireless
communication medium. In this paper we compared
the simulation results of the K-mean
mean techniques for
different numbers of nodess like 50, 100 and 150.
Keyword: K-mean, Nodes, Clusters.
I.
INTRODUCTION
The Wireless sensor networks (WSN) are highly
distributed networks of small, lightweight nodes and
deployed in large numbers to monitor the
environment parameters or system by the
measurement of physical parameters such as
temperature, pressure, or relativee humidity [2]. Each
node of the network consists of three subsystems: the
sensor subsystem which senses the environment, the
processing subsystem which performs local
computation on the sensed data, and the
communication subsystem which is responsible for
message exchange with neighbour sensor nodes.
While individual sensors have limited sensing region,
processing power, and energy, networking a large
numbers of sensors gives rise to robust, reliable, and
accurate sensor network covering a wider region [2]
[2].
Wireless Sensor Networks (WSNs) typically consist
of a large number of low-cost,
cost, low
low-power and
multifunctional wireless sensor nodes. Nodes are
equipped with sensing, communication and
computation capabilities, where they communicate via
a wireless medium
m and work collaboratively to
accomplish a common task.
To get more efficient and effective result of K-mean
K
algorithm there have been a lot of research happened
in previous day. All researchers worked on different
view and with different idea. Krishna and Murty[4]
proposed the genetic K-means(GKA)
means(GKA) algorithm which
integrate a genetic algorithm with K-means
K
in order to
achieve a global search and fast convergence.
Components of sensor node
Wireless sensor networks (WSNs) have gained worldworld
wide consideration
ation in recent years, particularly with
the proliferation in Micro-Electro
Electro-Mechanical systems
(MEMS) technology which has facilitated the
development of intelligent sensors. Sensor node as
shown in Figure 1 may also have additional
application dependent components such as a location
finding system, power generator and mobilize [1].
The analog signals produced by the sensors based on
the observed phenomenon are transformed to digital
signals by ADC, and then fed into the processing unit.
The processing unit,
t, which is generally associated
with a small storage unit, manages the events that
make the sensor node collaborate with other nodes to
carry out the assigned sensing tasks. A transceiver
unit connects the node to the network [1]. The main
job of sensor node
ode in a sensor field is to detect the
events, perform quick local data processing, and
broadcast the data. Energy consumption is a key issue
in wireless sensor networks (WSNs). In a sensor node,
energy is consumed by the power supply, the sensor,
the computation
utation unit and the broadcasting unit. The
wireless sensor node, being a microelectronics device,
can only be equipped with a limited power source
(≤0.5 Ah, 1.2 V) [1].
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 6 | Sep-Oct
Oct 2018
Page: 1215
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
2456
Fig 1: Components of a sensor node [1]
Density based Clustering algorithms: Data oobjects are
categorized into core points, border points and noise
points. All the core points are connected together
based on the densities to form cluster. Arbitrary
shaped clusters are formed by various clustering
algorithms such as DBSCAN, OPTICS, DBCLAS
DBCLASD,
GDBSCAN, DENCLU and SUBCLU [3].
Fig. 2: K-Means Algorithm [5, 6]
II.
K-MEANS CLUSTERING
There are many algorithms for partition clustering
category, such as kmeans clustering (MacQueen
1967), k-medoid
medoid clustering, genetic kk-means
algorithm (GKA), Self-Organizing
Organizing Map (SOM) and
also graph-theoretical
theoretical methods (CLICK, CAST) [6].
k-means is one of the simplest unsupervised
learning algorithms that solve the well known
clustering problem, in the procedure follows a simple
and easy way to classify a given data set through a
certain number of clusters (assume k clusters) fixed
apriority. There are main idea is to define k centre’s,
one for each cluster. These centers should be placed in
a cunning way because of different location causes
different result. So results, the better choice is to place
them as much as possible far away from each other.
For the next step is to take each point belonging to
a given data set and associate it to the nearest centre.
When no point is pending, the first step is completed
and an early group age is done.
A.1 K-mean
mean Algorithm Process
1. Select K points as initial centroid.
2. Repeat.
3. Form k clusters by assigning all points to the
closest centroid.
4. Recomputed the centroid of each cluster Until the
centroid do not change.
III.
Result Analysis of K-Means Technique
k-means techniques is one of the simplest
unsupervised learning algorithms that solve the
well known clustering problem. We consider five
access points of K-means
means technique for 150, 50 and
100 Nodes as shown in Figure 3, Figure 4, and Figure
5, respectively.
After we have these k new cancroids, a new binding
has to be done between the same data set
points and the nearest new centre. A loop has been
generated. As a result of this loop we may notice
that the k canters change their location step by step
until no more changes are done or in other words
canters do not move any more.
A. The process of k-means
means algorithm:
This part briefly describes the standard kk-means
algorithm. K-means
means is a typical clustering algorithm
in data mining and which is widely used for clustering
large set of data’s.
Fig. 3: K-means
means clustering map for 150 Nodes
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 6 | Sep-Oct
Oct 2018
Page: 1216
International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
2456
1000
50
900
15
800 31
600
12
9
23
43
39
8
36
21
700
32
26 29
2
40
16
30
24
6
14
46
10
27
33
48
500
400
4
20
11
28
7
25
22
300
19
3
44
100
45
37
47
38
200
0 42
0
100
34
13
17
18
1
35
41 5
49
200
300
400
500
600
700
800
900
1000
Fig. 4: K-means
means clustering map for 50 Nodes
1000
50
95 67
149
148
137
61
117
112
89 34
74 6440
69
60
37
82
107
47
126
146 13872
84
39
800
105
133
104
100
28 110
131 122 75 19
33
139
700
27
136
20
93
76
7
26
147
142 68
119
125 45
94
66
18
99
121
109 11 114
10
600
129
56
15
113
145 30
130 116 96
132
500
83
134
140
29
85
32 23 59
1 86
51 87 22
73
128
81
103
8897
400 124120 55
123
6
54 101
108
143 17 127
24
77
52
118
53
4
300
91
35
62
42
6541
115 38
200
98
111
80
14
135
4878 46
49
63
25
144 7971
31
57
141
100 8 21
44 36
150
16
92
43
2
9 58
5 13
102
70 3
90 12
0
0
100 200 300 400 500 600 700 800 900 1000
900
106
Fig. 5: K-means
means clustering map for 150 Nodes
IV.
CONCLUSION
The K-means
means algorithm is a popular clustering
algorithm. Distance measure plays a important rule on
the performance of this algorithm. Also, we will try to
extend future this work for another partition clustering
algorithms like K-Medoids,
Medoids, CLARA and CLARANS.
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