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. REFERENCES 1. Vibhav Kumar Sachan, Syed Akht Akhtar Imam “Energy-efficient 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 Overhead and Energy Consumption in Wireless Sensor Network using Different Clustering Techniques” IJSHRE, Vol. 2(1), 2014. 3. T. Mohana Priya, Dr. A.Saradh “An Improved KK 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. 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