Improvement in K-Means Clustering for Information Retrieval

In the field of data science and machine learning, data clustering is a potent technique that enables academics and businesses to glean valuable insights from massive and complicated datasets. Clustering algorithms can aid in locating patterns, anomalies, and trends that might not be immediately obvious in the raw data by grouping similar data points together. Making data-driven decisions in a variety of industries, from marketing and social media to healthcare and banking, can be really helpful with this. Clustering is the process of grouping data or unlabeled examples based on similarities among them to organize and get insights from the data by using various unsupervised machine-learning algorithms. The similarities are measured as per the features available in the data. More the number of features, the more complex it gets to measure similarity between them. K-means Clustering is an unsupervised machine learning which categorizes the data into K distinct groups of similar examples as it plots the samples in an N-dimensional plane based on the features and categorizes them while conversing from random start points using Euclidean distance measurement. This research work focuses on Information retrieval using K-means Clustering and other data retrieval algorithms as data retrieval. It contains a comparative analysis between the traditional K-means, the Ranking, and Query Redirection Method, and the Term Frequency-Inverse Document Frequency method to suggest a better approach for data mining and information retrieval.