Introduction

The advent of artificial intelligence (AI) has fundamentally transformed the field of data mining, enabling the extraction of valuable insights from increasingly large and complex datasets. Data mining is the process of discovering patterns and knowledge from large amounts of data, and its significance has grown with the exponential increase in data generation across various sectors, including healthcare, finance, marketing, and more (1). AI-driven techniques, particularly machine learning (ML) and deep learning (DL), have emerged as powerful tools for enhancing data mining processes, allowing for automated pattern recognition and predictive analytics that were previously unattainable (2,3). The integration of AI into data mining has enabled organizations to make data-driven decisions with greater accuracy and efficiency. For instance, in healthcare, AI algorithms analyze patient data to predict disease outbreaks and optimize treatment plans (4. In finance, machine learning models are employed to detect fraudulent transactions in real-time (5). The application of these technologies is vast, leading to improved operational efficiencies and innovative solutions across various domains (6). Despite the numerous advantages, the deployment of AI-driven data mining techniques also presents challenges, such as data privacy concerns, algorithmic bias, and the need for interpretability in model predictions (7). As the field continues to evolve, it is crucial to explore these methodologies comprehensively, addressing both their capabilities and limitations. This review aims to provide an in-depth examination of AI-driven data mining techniques, elucidating their impact on data analysis and decision-making processes, while also highlighting future directions for research and application.

Methods and Diagram

2.1 Data Mining Techniques

AI-driven data mining techniques use advanced artificial intelligence algorithms to automatically discover patterns, trends, and valuable insights from large datasets. These techniques enhance the efficiency and accuracy of data analysis across various sectors. This summary explores the strengths, weaknesses, applications, and key fields where AI-driven data mining is making a significant impact, providing a concise overview of how these technologies are transforming industries like finance, healthcare, marketing, and beyond. Below table show summary of each technique and its strengths, weaknesses, applications and fields.

2.2 Block Diagram

Below Block Diagram shows the life cycle diagram of data mining procedures with a list of AI-driven data mining techniques. It visually organizes information into structured blocks connected by lines, showing relationships between the techniques and their attributes. It includes main procedures of data mining:

1. Data Collection
2. Data Preprocessing
3. Data Transformation
4. Data Mining
5. Evaluation
6. Knowledge Representation

Literature Review

Many studies have been reported on developing AI techniques for driven data mining in order enhancing the data mining process by automating tasks such as pattern discovery, anomaly detection, classification, and clustering. Most of them are listed below.

1. Han et al. (2011) (1)

Method/Technique: Introduced techniques for frequent pattern mining, particularly decision trees and association rule learning, in traditional data mining. Result: Their work became foundational for data mining algorithms, setting the stage for future integration with AI techniques. These early methods, though powerful, lacked the scalability and flexibility AI now provides.

2. Mitchell (1997) (8)

Method/Technique: Explored early machine learning methods like decision trees and neural networks in the context of data mining. Result: Mitchell demonstrated how machine learning can automate data classification and predictive modeling, which significantly enhanced the performance and adaptability of data mining processes.

3. LeCun et al. (2015) (9)

Method/Technique: Applied deep learning, especially convolutional neural networks (CNNs), to high-dimensional data such as images, leading to breakthroughs in areas like image recognition.

Result: Deep learning models dramatically outperformed traditional methods in data mining tasks involving complex, unstructured data, marking a significant advancement in AI-driven data mining.

4. Zhou et al. (2020) (10)

Method/Technique: Investigated hybrid models that integrate traditional data mining techniques (e.g., clustering) with machine learning and deep learning approaches. Result: The study showed that hybrid models outperform singular approaches, particularly in classification and prediction, by leveraging the strengths of both AI and traditional methods.

5. Xiao et al. (2019) (11)

Method/Technique: Proposed privacy-preserving AI-driven data mining techniques, such as federated learning and differential privacy, to protect sensitive information. Result: Their approach helped resolve ethical concerns by ensuring that AI-driven data mining could be applied in sensitive domains, such as healthcare, without compromising privacy.

 6.Singh et al. (2021) (12)

Method/Technique: Focused on developing explainable AI techniques within data mining to address the issue of model interpretability. Result: Their research improved the transparency of complex AI-driven models,  

Conclusion

   The conclusions drawn include the identification of emerging AI mining techniques and their expanding applications across various industries. It evaluates the effectiveness of these methods in extracting valuable insights from large datasets and discusses key challenges, such as data privacy issues, algorithmic bias, and the necessity for interpretability in AI models. Future directions involve recommending research areas that integrate AI with other technologies and the development of more robust frameworks. Ethical considerations emphasize the implications of using AI in data mining and the importance of responsible practices. Suggestions include frameworks or models to enhance the implementation of AI-based mining techniques, highlighting the need for collaboration among data scientists, domain experts, and ethicists to improve outcomes. These conclusions aim to provide a thorough understanding of the current landscape of AI-based mining and its implications for future research and applications.

References

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