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    Introduction Cluster analysis has many different algorithms and methods to classify objects(Saunders, 1994). One of the challenges faced by the researchers in different areas is to organize their data which is possible by cluster analysis, it is a data analysis tool which focus on classifying the different objects into groups such that the degree of association of the objects in a same group is highest if they belong and least if they do not belong. Cluster analysis is a simple term, it does not

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    Forensic Analysis Implementation Ms. Rajnee Kanoje1, Dr. S. D. Choudhari2 MTech. CSE, SBITM COE, Betul, Professor SBITM COE, Betul Email - rajnee03kanoje@gmail.com, choudhari.sachin1986@gmail.com Abstract: Now days, criminals frequently use all latest technologies to commit serious crimes like cracking sites, fraud in different domains, prohibited access etc. Thus, the inquiry of such cases is very difficult and more significant task. So, we need to do the

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    Abstract—A tumor is the growth in the abnormal tissue of the brain which causes damage to the other cells necessary for functioning. Detection of brain tumor is a difficult task, as there are various techniques involved in it. The active imaging resource used for brain tumor detection is Magnetic Resonance Imaging (MRI). It is necessary to use technique which can give the accurate location and size of the tumor. There are various algorithms proposed for brain tumor detection, this paper presents

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    • Distance between clusters • Nearest neighbor (single linkage). In this measure the similarity between two clusters is defined as the smallest distance between two objects in different clusters. Distance between cluster A and cluster B is the minimum amongst the following pairs (1,5), (1,6), (1,7), (2,5), (2,6), and (2,7). In each iteration, the distance between two different clusters is equal to the distance between its closest members. • Furthest neighbor (complete linkage). With this similarity

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    Energy-Efficient Cluster Formation Techniques: A Survey Jigisha M. Patel Department of Computer Engineering C.G.P.I.T, Uka Tarsadia University Bardoli, India pateljigisha884@gmail.com Mr. Achyut Sakadasariya Department of Computer Engineering C.G.P.I.T, Uka Tarsadia University Bardoli, India achyut.sakadasariya@utu.ac.in Abstract—In wireless sensor network (WSN), many novel architectures, protocols, algorithms and applications have been proposed and implemented for energy efficiency

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    choosing the elements from the inhabitants through identical odds. Types of Random Sampling: There are five types of random sampling. Type 1: Simple random sampling. Type 2: Systematic random sampling. Type 3: Stratified random sampling. Type 4: Cluster random sampling. Type 5: Multistage random sampling. Explanation: Type 1: Simple random sampling: The simple random sampling is one of the types of sampling. The choosing element units are depends on the population with the identical chances being

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    Specification Operating System K-Strange 2 clusters (Sec) K-Strange 3 clusters (Sec) K-means 2 clusters (Sec) K-means Clusters (Sec) Intel(R) Core (TM) i5-4210U CPU @ 1.70 GHz 2.40 GHz RAM:- 8.00 GB Windows 64-bit Operating System, x64-based processor. 0.09 0.122 0.098 0.185 Intel(R) Core (TM) i3-4130U CPU @ 3.40 GHz 3.40 GHz RAM:- 4.00 GB Windows 64-bit Operating System, x64-based processor. 0.08 0.156 0.086 0.096 2.7 GHZ Dual Core Intel Core i5 RAM: 8 GB Mac OS (10.12) sierra 0.04

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    Student Intervention

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    numerous in Michigan, it can be concluded that the study was a cluster sampling. Cluster sampling can be a disadvantage because it provides less precision than any other type of sampling. In addition, cluster sampling is not cost-efficient. No specific details were given as to how participants were recruited. For the most part, it seems that, the school took charge and the researchers simply created the study. In addition to being a cluster sampling, the study followed the Participatory Action Research

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    number of clusters. Minimization of a performance index is the primary basis of K-means Algorithm, which is defined as the sum of the squared distances from all points in a cluster domain in the cluster center. Initially K random cluster centers are chosen. Then, each sample in the sample space is assigned to a cluster based on the minimum distance to the center of the cluster. Finally the cluster centers are updated to the average of the values in each cluster. This is repeated until cluster centers

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    Optimization clustering As one of the most popular and widely used data mining techniques, cluster analysis is mainly divided into hierarchical clustering and partitional clustering, which are carried out in a supervised or unsupervised way to separate data into different groups based on similar characteristics. Both the hierarchical and partitional clustering have advantages and drawbacks. Especially, the efficiency and accuracy are the primary challenges that clustering analysis has to face. For

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