Performance Analysis of K mean Clustering Map for Different Nodes

1. International Journal of Trend in International Open Access Journal International Open Access Journal | www.ijtsrd.com International Journal of Trend in Scientific Research and Development (IJTSRD) Research and Development (IJTSRD) www.ijtsrd.com ISSN No: 2456 ISSN No: 2456 - 6470 | Volume - 2 | Issue – 6 | Sep 6 | Sep – Oct 2018 Performance Analysis of K Performance Analysis of K-mean Clustering Map for Different Nodes mean Clustering Map for Different Nodes Subham Bhawsar, Aastha Hajari Subham Bhawsar, Aastha Hajari Embedded System and VLSI Design Shiv Kumar Singh Institute of Technology Shiv Kumar Singh Institute of Technology & Science (SKSITS), Indore,Madhya Pradesh edded System and VLSI Design, Department of Electronics and Communication Engineering nd Communication Engineering Madhya 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 techniques for different numbers of nodes 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 measurement of physical parameters such as temperature, pressure, or relative humidity [2]. Each node of the network consists of three subsystems: the sensor subsystem which senses the environment, the processing subsystem computation on the sensed 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] Wireless Sensor Networks (WSNs) typically consist of a large number of low-cost, low multifunctional wireless sensor nodes. Nodes are equipped with sensing, computation capabilities, where they communicate via a wireless medium and work collaboratively to accomplish a common task. To get more efficient and effective result of K algorithm there have been a lot of research happened in previous day. All researchers worked on different view and with different idea. Krishna proposed the genetic K-means(GKA) algorithm which integrate a genetic algorithm with K achieve a global search and fast convergence. Components of sensor node Wireless sensor networks (WSNs) have gained world wide consideration in recent years, particularly with the proliferation in Micro-Electro (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, 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 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 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]. WSNs is a group of inexpensive and autonomous sensor nodes interconnected and work together to monitor various environmental conditions such as pressure, and other climate changes. Wireless sensor nodes are randomly installed and communicate themselves through the wireless communication medium. In this paper we compared and effective result of K-mean 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] means(GKA) algorithm which integrate a genetic algorithm with K-means in order to achieve a global search and fast convergence. mean techniques for s like 50, 100 and 150. Wireless sensor networks (WSNs) have gained world- ation in recent years, particularly with Electro-Mechanical systems (MEMS) technology which has facilitated the development of intelligent sensors. Sensor node as shown in Figure 1 may also have additional 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. 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 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, utation unit and the broadcasting unit. The wireless sensor node, being a microelectronics device, can only be equipped with a limited power source The Wireless sensor networks (WSN) are highly distributed networks of small, lightweight nodes and deployed in large numbers to monitor the environment parameters measurement of physical parameters such as or or system system by by the the e humidity [2]. Each node of the network consists of three subsystems: the sensor subsystem which senses the environment, the processing subsystem computation on 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]. Wireless Sensor Networks (WSNs) typically consist which which sensed performs data, data, performs local the the local and and cost, low-power and multifunctional wireless sensor nodes. Nodes are equipped with sensing, computation capabilities, where they communicate via m and work collaboratively to communication communication and and @ IJTSRD | Available Online @ www.ijtsrd.com www.ijtsrd.com | Volume – 2 | Issue – 6 | Sep-Oct 2018 Oct 2018 Page: 1215

2. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456 International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456 International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 Fig 1: Components of a sensor node [1] Fig 1: Components of a sensor node [1] Density based Clustering algorithms: Data o 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 GDBSCAN, DENCLU and SUBCLU [3]. II. K-MEANS CLUSTERING There are many algorithms for partition clustering category, such as kmeans clustering (MacQueen 1967), k-medoid clustering, algorithm (GKA), Self-Organizing Map (SOM) and also graph-theoretical methods (CLICK, CAST) [6]. k-means is one of the simplest unsupervised learning algorithms that solve the well clustering problem, in the procedure follows a simple and easy way to classify a given data set 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 a cunning way because of different location causes different result. So results, the better choice 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. After we have these k new cancroids, a new binding has to be done between the points and the nearest new centre. A loop has been generated. As a result of this loop we that the k canters change their location step by step until no more changes are done or in canters do not move any more. A. The process of k-means algorithm: This part briefly describes the standard k algorithm. K-means is a typical clustering algorithm in data mining and which is widely used for clustering large set of data’s. Density based Clustering algorithms: Data objects 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, DBCLASD, GDBSCAN, DENCLU and SUBCLU [3]. Fig. 2: K-Means Algorithm [5, 6] Algorithm [5, 6] A.1 K-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- k-means techniques is one of unsupervised learning algorithms well known clustering problem. We consider access points of K-means technique for 150, 50 and 100 Nodes as shown in Figure 3, Figure 4, and Figure 5, respectively. There are many algorithms for partition clustering category, such as kmeans clustering (MacQueen medoid clustering, Organizing Map (SOM) and theoretical methods (CLICK, CAST) [6]. the simplest unsupervised mean Algorithm Process Select K points as initial centroid. genetic genetic k k-means Form k clusters by assigning all points to the Recomputed the centroid of each cluster Until the the well known clustering problem, in the procedure follows a simple to classify a given data set through a clusters (assume k clusters) fixed Means Technique one of the simplest algorithms that solve the is to define k centre’s, should be placed in known clustering problem. We consider five means technique for 150, 50 and Figure 3, Figure 4, and Figure because of different location causes choice is to place far away from each other. step is to take each point belonging to given data set and associate it to the nearest centre. the first step is completed After we have these k new cancroids, a new binding the same same data data set set the nearest new centre. A loop has been this loop we may notice that the k canters change their location step by step in other words means algorithm: This part briefly describes the standard k-means means is a typical clustering algorithm in data mining and which is widely used for clustering Fig. 3: K-means clustering map for 150 Nodes means clustering map for 150 Nodes @ IJTSRD | Available Online @ www.ijtsrd.com www.ijtsrd.com | Volume – 2 | Issue – 6 | Sep-Oct 2018 Oct 2018 Page: 1216

3. International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456 International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456 International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470 Consumption in Wireless Sensor Network using Different Clustering Techniques” IJSHRE, Vol. 2(1), 2014. 3.T. Mohana Priya, Dr. A.Saradh “An Improved K means Cluster algorithm using Map Reduce Techniques to mining of inter and intra cluster datain Big Data analytics” International Journal of Pure and Applied Mathematics, Volume 119 No. 7 2018, pp. 679-690. 4.Nidhi Singh, Divakar Singh “Performance Evaluation of K-Means and Heirarichal Clustering in Terms of Accuracy and Running Time” International Journal of Computer Science and Information Technologies, Vol. 3 (3) , 2012, pp. 4119-4121 5.Dibya Jyoti Bora, Dr. Anil Kumar Gupta “Effect of Different Distance Performance of K-Means Experimental Study in Matlab” Dibya Jy et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, pp. 2501-2506. 6.Shaeela Ayesha, Tasleem Mustafa, Ahsan Raza Sattar & M.Inayat Khan, “Data Mining Model for Higher Education System “,Europe Scientific Research, ISSN 1450 No.1 ,2010, pp.27. 7.Amrinder Kaur, “Simulation of Low Energy Adaptive Clustering Hierarchy Protocol for Wireless Sensor Network”, Volume 3, Issue 7, July 2013. 8.Amitabh Basu , Jie Gao, Joseph S. B. Mit Girishkumar Sabhnani , “Distributed localization using noisy distance and angle information” 7th AC Minternational symposium on Mobile ad hoc networking and computing, 2006,pp. 262 9.Rui Zhang, Myung J. “Mobile sink update local information through these Aps”, 2008. 10.Shiv Prasad Kori, “Performance Comparison in Terms of Communication Overhead for Wireless Sensor Network Based on Clustering Technique”, Volume 4, Issue 3, ISSN (Online): 2249 ISSN (Print): 2278–4209. 11.Labisha R. V, Baburaj E, “Energy Efficient Clustering Algorithms Networks-An Analytical Number3, May 2014. 12.E. Ekici , S. Vural, J. McNair, D. Al probabilistic location verification in randomly deployed wireless sensor networks “ Elsevier Science Publishers B. V, 2006 ,pp.,195 Science Publishers B. V, 2006 ,pp.,195-209 Consumption in Wireless Sensor Network using Different Clustering Techniques” IJSHRE, Vol. 1000 50 32 32 900 12 43 34 39 8 9 23 13 15 A.Saradh “An Improved K- 800 31 36 means Cluster algorithm using Map Reduce Techniques to mining of inter and intra cluster datain Big Data analytics” International Journal of Pure and Applied Mathematics, Volume 119 No. 40 21 26 700 6 29 2 16 46 14 14 30 600 10 24 27 4 20 33 500 11 28 48 400 Nidhi Singh, Divakar Singh “Performance Means and Heirarichal Clustering in Terms of Accuracy and Running Time” onal Journal of Computer Science and Information Technologies, Vol. 3 (3) , 2012, pp. 7 25 19 17 22 3 300 45 18 47 37 38 200 44 1 35 5 41 100 49 42 0 0 100 200 300 400 500 600 700 800 900 1000 Dibya Jyoti Bora, Dr. Anil Kumar Gupta “Effect of Different Distance Fig. 4: K-means clustering map for 50 Nodes means clustering map for 50 Nodes Measures Algorithm: Means Algorithm: Measures on on the the An An Experimental Study in Matlab” Dibya Jyoti Bora et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) 1000 148 50 95 137 137 67 149 61 117 106 40 112 89 900 34 74 64 69 60 107 37 82 126 47 146 72 133 84 39 138 800 105 104 Shaeela Ayesha, Tasleem Mustafa, Ahsan Raza Sattar & M.Inayat Khan, “Data Mining Model for Higher Education System “,European Journal of Scientific Research, ISSN 1450-216X Vol.43 75 100 110 28 147 131 19 136 33 139 700 27 142 122 20 93 76 7 26 94 68 119 125 66 45 114 18 99 121 109 10 129 11 600 56 15 113 145 130 30 96 116 132 500 83 134 140 29 87 97 85 59 73 86 22 32 32 1 23 51 Amrinder Kaur, “Simulation of Low Energy Adaptive Clustering Hierarchy Protocol for Wireless Sensor Network”, Volume 3, Issue 7, 81 128 103 124 88 400 123 120 55 6 108 101 127 54 77 143 17 24 52 118 53 4 300 91 35 115 62 42 65 38 41 200 98 111 46 80 144 14 71 135 14 135 Amitabh Basu , Jie Gao, Joseph S. B. Mitchell, Girishkumar Sabhnani , “Distributed localization using noisy distance and angle information” 7th AC Minternational symposium on Mobile ad hoc networking and computing, 2006,pp. 262 – 273. 48 78 49 63 79 141 25 31 57 100 8 92 21 44 36 150 16 43 2 58 9 3 12 13 5 102 70 90 90 0 0 100 200 300 400 500 600 700 800 900 1000 Fig. 5: K-means clustering map for 150 Nodes means clustering map for 150 Nodes Lee, Seong-Soon Joo, local information through IV. The K-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, CLARA and CLARANS. REFERENCES 1.Vibhav Kumar Sachan, Syed Akht “Energy-efficient Communication Methods in Wireless Sensor Networks: A Critical Review” International Journal of Computer Applications (0975 – 8887) Volume 39– No.17, February 2012. 2.Shweta Bhatele and Lalita Bargadiya “Evaluation of Communication CONCLUSION 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 Medoids, CLARA and CLARANS. 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