A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques

TLDR

The rapid growth of unstructured text demands efficient techniques to extract meaningful information, making text mining a critical field. The paper outlines core text mining tasks—pre‑processing, classification, and clustering—and discusses their application in biomedical and healthcare contexts. They employ standard text mining techniques such as pre‑processing, classification, and clustering to uncover patterns in large text datasets.

Abstract

The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains.