Cluster analysis
Discussion 5
A number of different algorithms and methods for grouping similar objects within their respective categories are used in cluster analysis. In many fields of inquiry, the general issue facing researchers is how observed data can be structured for the creation of taxonomies into concrete structures. Cluster analysis is an EDA tool designed to arrange different objects in clusters to achieve the highest possible level of correlation between two objects if they belong to the same category and otherwise to the minimum.
By offering an explanation or interpretation, the cluster analysis can be used to identify structures in data. Cluster analysis identifies data structures by specifying why they exist.
In almost every area of everyday life, we deal with clustering. For instance, a group of diners sharing the same table can be viewed as a group of people in the restaurant. Articles similar in foodstuffs are produced in the same or surrounding places, such as different types of meat or vegetables. There are numerous examples where clustering has a significant role to play.
The maximum or the minimum number of the mean component or centroid in the data depends on how one visualize the data. If two groups can be visualized, then 2 is the minimum and a maximum number of a mean component in the data. A target number k will be defined, which refers to the number of mean components one requires in his or her dataset. The middle of the cluster is the hypothetical or actual location. By reducing the in cluster sum of squares, every data point is allocated to each of the clusters estimated. The algorithm determines the median variable and the number of mean components and assigns all data points to the next cluster while maintaining the smallest possible mean component.
References
Ayodele, T. O. (2010). Types of machine learning algorithms. New advances in machine learning, 19-48.
Kassambara, A. (2017). Practical guide to cluster analysis in R: Unsupervised machine learning (Vol. 1). STHDA.