A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Abstract

Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.

Application of Clustering-Based Data Mining for the Assessment of Nutritional Status in Toddlers at Community Health Centers

“…Afterward, unnecessary labels or information that could disrupt the data mining process are removed [21]. 2) Data Integration, the next stage involves data integration, where data that is separated into multiple tables is merged into one [22]. 3) Data Selection, the subsequent stage is data selection, where data or attributes needed for analysis and data mining are chosen.…”

Combating the Challenges of False Positives in AI-Driven Anomaly Detection Systems and Enhancing Data Security in the Cloud

“…Unsupervised methods, such as clustering algorithms (e.g., k-means or DBSCAN), can identify patterns and group similar data points without prior labels. These groups can then be used as input for supervised learning models like neural networks or decision trees, which further refine the detection process by learning from labeled data [78,79]. This combination allows the system to benefit from the exploratory nature of unsupervised learning while harnessing the accuracy of supervised learning [80].…”

Using Medical Data and Clustering Techniques for a Smart Healthcare System

“…Ezugwu et al [7] and Saxena et al [8] reported that clustering techniques can be divided into two major categories, namely, hierarchical clustering algorithms and partition clustering algorithms. More clustering categories, including grid clustering, density clustering, and model clustering, were proposed by Chaudhry et al [9] and Oyewole and Thopil [10]. K-means and hierarchical clustering techniques are the most widely used algorithms in the literature.…”

“…It can be observed that most studies dealt with a single disease, and K-means was commonly used as a popular clustering technique for analyzing medical data. The clustering approaches can be generally classified into categories: hierarchical clustering algorithms and partition clustering algorithms [7][8][9][10]. This study employed four clustering methods, K-means (KM), hierarchical clustering (HC), the K-means autoencoder (AEKM), and the K-means self-organizing map (SOMKM), to analyze medical data.…”