Data Mining Systems Processes, and Tools for Pattern Identification and Trends in Large Datasets

Abstract

In today's data-driven world, the ability to extract meaningful insights from large datasets is paramount for organizations across various industries. However, this research paper aims to bridge the gap of lack of finding the exact meaningful information from a large data set in knowledge by investigating the efficiency, scalability, and accuracy of existing data mining systems in handling large-scale data to draw meaningful insights in it with an objective to; explore the processes and tools used in data mining systems for pattern identification and trend analysis in large datasets. Through an extensive review and analysis of existing literature, with an open ended survey questionnaire to understand the more in details why the findings of the existing literatures are, this research paper provides a comprehensive understanding of data mining systems, processes, and tools, equipping researchers, practitioners, and decision makers with the knowledge and insights needed to harness the power of large datasets for pattern identification and trend analysis, thereby facilitating informed decision-making and strategic planning in a data-driven world where the research revealed that the performance of data mining tools and algorithms in terms of scalability and efficiency varies depending on factors such as algorithm complexity, hardware resources, and dataset characteristics though some tools struggle with scalability due to computational limitations, while others are specifically designed to handle large datasets efficiently, strategies such as feature engineering, cross-validation, and ensemble learning methods., is the major strategies employed to enhance the accuracy and reliability of pattern identification and trend analysis in large datasets using data mining techniques.