The field of Information retrieval is so vast, and many efforts are being geared towards the advancement of Search Engine Optimization (SEO). According to (Globalneswire,2022) Search Engine Optimization Services market is expected to reach $134.26 billion in 2026 at a CAGR of 20.7%. This is good news and the opportunities in this research area is humongous.
Advancement in clustering is also expected to rise, especially in biological innovation, Clustering algorithms have been successfully used in molecular biology in recent years for the analysis of gene expression data. Genes are divided into groups based on the similarity between their expression profiles using clustering methods such as K-means, hierarchical clustering, SOM, etc. In this manner, genes with similar functions are found. Because of cutting-edge technologies like Microarray, the amount of laboratory data generated for molecular biology is increasing dramatically every year. To analyze this enormous volume of biological data, new efficient and effective clustering techniques must be created (Yi Lu, et al., 2004).
Searching for content (often documents) of an unstructured type (mostly text) that satisfies an information demand from vast collections is known as information retrieval (IR) (usually domiciled on computers) (Cambridge University,2009).
Metrics for information retrieval (IR) are used to assess how well a system retrieves data from a database or a group of documents. In search engines, a user-provided query is compared to many potential results, some of which are relevant and some of which are not.
This topic aims at implementing a modern retrieval system with focus on vertical search engine and its related preprocesses, such as tokenization, stemming, stop words and the use of TF-IDF for the ranking. K means Cluster Algorithm is also considered as an algorithm of choice for some documents copied across several RSS feeds (Sport, Health, and Politics).
Keywords: TF-IDF, Vertical Search Engine, stop words, Tokenization, stemming, and K means Cluster.