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Over the course of time, a tremendous amount of data is accumulated. Information extraction is one of the most time-consuming processes because it varies greatly depending on the user's requirements. Data mining's varied approaches are employed to compile relevant data and present it in a digestible fashion for end users. Clustering and classification are two data mining techniques used to uncover previously unseen patterns and insights.This summary discusses the use of data mining techniques, specifically clustering and classification, to extract relevant information from accumulated data. It…mehr

Produktbeschreibung
Over the course of time, a tremendous amount of data is accumulated. Information extraction is one of the most time-consuming processes because it varies greatly depending on the user's requirements. Data mining's varied approaches are employed to compile relevant data and present it in a digestible fashion for end users. Clustering and classification are two data mining techniques used to uncover previously unseen patterns and insights.This summary discusses the use of data mining techniques, specifically clustering and classification, to extract relevant information from accumulated data. It highlights the importance of selecting a suitable clustering algorithm and introduces the concept of using a genetic algorithm to improve the k-means clustering method. The proposed method aims to optimize the clustering process and demonstrates its effectiveness through a scenario-based test. The summary concludes by suggesting future research to further optimize the k-means algorithm using various evolutionary methods.
Autorenporträt
Gaurav Dwivedi ist Assistenzprofessor an der Fakultät für CSE/CA der United University, Prayagraj. Er verfügt über 5 Jahre Lehrerfahrung und hat einen M.Tech- und B.Tech-Abschluss in CSE. Mit seinen Patenten und Veröffentlichungen in internationalen Fachzeitschriften sowie Vorträgen auf internationalen Konferenzen hat er zur Forschung beigetragen.