Identification of Voting Patterns using Clustering Methodology

The act of separating a population or set of data points into a few groups or clusters so that data points in the same group are more like each other and distinct from data points in other groups is known as clustering. The purpose of this study is to categorize the respondents to identify groups with similar attitudes about science and technology and analyze their views. The difficulties of cluster analysis, determination of distance measure, number of clusters, and database structure have all been noted as possible issues with cluster analysis. To explore the respondents' grouping tendencies, several clustering approaches such as K-means, Hierarchical clustering, and so on are utilised The Hierarchical Clustering methodology itself may provide the analyst with the ideal number of clusters; human participation is not necessary. Dendrograms provide in clear imagery that is useful and simple to comprehend The centroids are computed by the K-means clustering method, which then iterates until it finds the ideal centroid It presumes that there are already known quantities of clusters. The flat clustering algorithm is another name for it. Since the data is binary, the clustering methods may be used to group the respondents. The clustering methods will be applied to the survey data by tracking the resultant decisions. Currently, all clustering algorithms have been used and it has been discovered that the data contains three or four clusters, each of which specifies a voting pattern that may be of interest.