Advancing Information Retrieval

Abstract

Information retrieval (IR) is booming because any application dealing with knowledge must retrieve relevant information from a huge data collection. The clustering mechanism plays a vital role in efficiently mining data from massive datasets. During a search, the items that have similar characteristics are grouped together using this strategy so that they may be found and retrieved more quickly. Traditional clustering methods are not capable of producing the required results in an efficient manner. When used in conjunction with a pattern mining technique, clustering can significantly boost the effectiveness of a search. The pattern mining method improves the quality of the clusters produced by exploring the dataset for patterns comparable to one another. The primary emphasis of this study is placed on more recent breakthroughs in information retrieval methods, including clustering and pattern mining. The article examines the present state of the art in information retrieval by dividing it into a few different categories and discussing its implications. This paper provides an overview of the most recent developments in the information retrieval field. The comparative analysis outlines the benefits and limitations of many different retrieval algorithms utilized to obtain the information. Open questions, challenges, and emerging trends are studied thoroughly. We have implemented a k-Means clustering algorithm for document clustering. Performance is evaluated in terms of the number of clusters, SSE, and execution time for the 20Newsgroup document dataset, which works well for small-scale datasets. The research community can develop more efficient data retrieval techniques by focusing on this article’s challenges and future dimensions.