Improved Semantic Information and Extraction based Effective Pattern Discovery Mining in Bigdata Using Latent Semantic Indexing Model

Abstract

In the era of Big Data, extracting meaningful patterns and insights is pivotal for decision-making. Latent Semantic Indexing (LSI) emerges as a promising approach for uncovering semantic relationships in vast datasets. This document explores how LSI can be effectively applied for pattern discovery, emphasizing its capability to extract and represent semantic information from unstructured and structured data. Key challenges, methodologies, and potential applications are discussed to highlight the role of LSI in Big Data mining. Numerous data mining methods have been proposed for mining to find useful patterns in text documents. Though, how toeffectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since mostexisting text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. This paper proposed semantic information and extraction for effective pattern discovery mining in big data using LSI algorithm. Semantic Information and extraction stages’ using Latent Semantic Indexing (LSI) algorithm and patterns are organized in specific format then evaluates the term weights and discovered specific patterns in the set of documents.