SPSS- It is a statistical analysis and data management Software package. SPSS can take data from almost any type of file and use them to generate tabulated reports, charts, and plots of distributions and trends, descriptive statistics, and conduct complex statistical analyses.
Before start working on the actual software, it is necessary to go through some basics of the statistics involved. Basics of the statistics include terminologies like- Sample size, selection, mean, variance, standard deviation moreover it also includes frequency, cross tabulation, cluster analysis, chi square analysis, which we discussed in class.
First Day of the SPSS classes focused on some basic fundamental and how to use the SPSS software. When you open the software, two different windows can be opened while using SPSS,
The Data Editor –It is a spreadsheet in which you define your variables and enter data. Each row corresponds to a case while each column represents a variable. The title bar displays the name of the open data file or "Untitled" if the file has not yet been saved.
The Output Navigator- It displays the statistical results, tables, and charts from the analysis you performed. An Output Navigator window opens automatically when you run a procedure that generates output. In the Output Navigator windows, you can edit, move, delete and copy your results in a Microsoft Explorer-like environment.
Cluster Analysis-
Cluster analysis classifies a set of observations into two or more mutually exclusive unknown groups based on combinations of interval variables. The purpose of cluster analysis is to discover a system of organizing observations, usually people, into group, where members of the groups share properties in common. It is cognitively easier for people to predict behavior or properties of people or objects based on group membership, all of whom share similar properties. It is generally cognitively difficult to deal with individuals and predict behavior or properties based on observations of other behaviors or properties. The most important techniques for data collection are:
1. Cluster Analysis.
2. Discriminant Analysis.
Although both cluster and discriminant analysis classifies objects into categories, discriminant analysis requires one to know group membership for the cases used to decide the classification rule whereas in cluster analysis group membership for all the cases is unknown. In addition to membership, the number of groups is also generally unknown.
Steps in Cluster Analysis-
a) Select a measure of similarity.
b) Decision is to be made on the type of clustering technique to be used.
c) Type of clustering method for the selected technique is selected.
d) Decision regarding the number of cluster.
e) Cluster solution is interpreted.
Clustering Techniques in Wireless Sensor Networks-
Due to the communication devices on sensor nodes have limited battery capacity and transmission range, wireless sensor networks (WSNs) are considered to be energy constrained. In this paper, we propose a novel clustering algorithm called limiting member node clustering (LmC) algorithm to limit the number of member nodes for each cluster head by using a threshold value. The proposed clustering approach selects a cluster head based on a new cost function which considers the residual battery level, energy consumption and distance to the base station. In our experiments, we considered the transmission range of the base station in WSNs to improve the clustering performance. From experimental results, the proposed algorithm can efficiently achieve high number of successfully delivered packets as well as the longest network lifetime while give the shortest delay time and low energy consumption when compared with different existing algorithms.
Submitted By-
Roll no: 12060
Finance Batch
SIBM, Bangalore
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