Clustering data and reducing the dimensionality
The process of clustering involves grouping the population or data points into a number of groups so that the data points within each group are more similar to one another than the data points within other groups. Simply said, the goal is to sort any groups of people who share similar characteristics into clusters. It is frequently used in business analytics. How to arrange the enormous volumes of available data into useful structures is one of the issues that organizations are currently confronting.
Image segmentation, grouping web pages, market segmentation, and information retrieval are four examples of how clustering can help firms better manage their data. Data clustering is beneficial for retail firms since it influences sales efforts, customer retention, and customer shopping behavior.
The goal of the vector quantization technique known as “K-means clustering,” which has its roots in signal processing, is to...
