Data Mining Best Practices and Efficiency in Large Scale Data Mining: A Comprehensive Study

Abstract

This research aims to explore and establish best practices in data mining and analyze the efficiency of these practices when applied to large scale datasets. The study will focus on comparing various data mining techniques, tools, and frameworks to identify the most effective strategies for handling big data, improving processing time, and enhancing decision-making processes.

Introduction

Data mining is a critical process in extracting valuable information from large sets of data. As data volumes continue to grow exponentially, it becomes paramount to refine data mining techniques to handle large-scale data efficiently. This research will provide a comprehensive analysis of current data mining practices and develop guidelines for optimizing large scale data mining projects.

Objectives

1. Review Current Practices: To survey and synthesize the existing literature and case studies on large scale data mining.
2. Identify Efficiency Metrics: To define and utilize metrics that measure the efficiency and effectiveness of data mining processes.
3. Test and Compare Techniques: To empirically test various data mining methods and tools to determine their performance and scalability.
4. Develop Best Practices Guide: To formulate a set of best practices tailored for large scale data mining.
5. Identify the current gaps
6. To identify the role of artificial intelligence in solving the current gaps

Literature Review

• Theoretical Frameworks: Study existing theoretical models that underpin data mining methodologies.
• Technological Tools: Review various data mining tools like Hadoop, Spark, and TensorFlow, focusing on their scalability and efficiency.
• Case Studies: Analyze documented instances of large scale data mining projects across different industries to gather insights on challenges and solutions.