Authors:
April Thet Su, Hlaing Htake Khaung Tin, M. Sakthivanitha, S. Silvia Priscila
Addresses:
Faculty of Information Science, University of Information Technology, Yangon, Myanmar. Department of Information Technology, Vels Institute of Science, Technology and Advance Studies, Chennai, Tamil Nadu, India. Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India.
Abstract:
In recent years, the exponential growth of both structured and unstructured data has highlighted the importance of efficient knowledge discovery techniques. Among these, image mining and data mining play pivotal roles in extracting meaningful patterns and insights from different forms of information. At the same time, data mining focuses on structured datasets such as databases and transaction records, while image mining deals with unstructured visual content, requiring advanced feature extraction, pattern recognition, and machine learning methods. This paper presents a comparative analysis of image mining and data mining techniques, emphasising their methodologies, applications, and challenges. The comparison explores key dimensions, including data preprocessing, feature selection, algorithmic approaches, and application domains such as healthcare, security, business intelligence, and multimedia. Experimental results show clustering accuracy of 87% for data mining and image classification accuracy of 92% for image mining, highlighting the effectiveness of specialised approaches. The study highlights both the similarities and differences in knowledge discovery processes, demonstrating how integrating image mining and data mining can enhance decision-making in diverse fields. The findings provide a comprehensive understanding of their complementary roles, offering valuable insights for researchers and practitioners aiming to develop hybrid approaches to knowledge discovery.
Keywords: Data Mining; Image Mining; Knowledge Discovery; Machine Learning (ML); Social Media; Pattern Recognition; Digital Technologies; Structured Information; Image Classification.
Received: 24/10/2024, Revised: 09/12/2024, Accepted: 05/03/2025, Published: 07/12/2025
DOI: 10.64091/ATICL.2025.000234
AVE Trends in Intelligent Computer Letters, 2025 Vol. 1 No. 4 , Pages: 198-207