OVERVIEW OF TRANSFORMERS ROLE IN DATA MINING FROM UNSTRUCTURED DATA

Abstract

The rapid growth of Big Data has made it increasingly important to extract meaningful insights from unstructured sources such as text, audio, video, and emails. Traditional data mining techniques—like tokenization, clustering, classification, and association rule mining—have provided a basis for processing these complex data forms. However, they often struggle to capture the subtle semantic and contextual relationships that are inherent in unstructured data. In this article, we examine the limitations of these conventional methods and explore the impact of Transformer Neural Networks (TNNs) on unstructured data mining.

Transformer architectures have revolutionized the field by employing self-attention mechanisms and positional encodings, which allow for parallel processing of data. This new approach enables the creation of high-quality embeddings that capture both semantic and syntactic information. As a result, tasks such as sentiment analysis, topic modeling, and automated summarization are significantly enhanced. Additionally, integrating transformers into audio signal processing and email mining has led to notable improvements in automatic speech recognition and semantic analysis, effectively addressing some of the long-standing challenges in these areas. The findings discussed in this article highlight the potential of transformer-based approaches to not only overcome the limitations of traditional data mining methods but also to open the door to innovative applications across various fields. Future research directions include developing more computationally efficient transformer models and exploring hybrid approaches that combine traditional techniques with advanced neural architectures. These efforts will ultimately push the boundaries of what is possible in unstructured data mining.