Data Mining

Overview

Data mining is the computational process of discovering useful patterns, relationships, and structures within large datasets. Drawing on statistics, machine learning, and database technology, it transforms raw data from sources such as databases, warehouses, and the web into actionable knowledge by identifying correlations, trends, clusters, and anomalies. Core tasks include classification, in which models assign records to categories; clustering, which groups similar objects, including spatial data; association and frequent-pattern mining, which uncovers items, itemsets, and sequential patterns that co-occur; and outlier detection. Algorithms central to the field include decision-tree learners such as ID3 and C4.5, alongside methods for mining closed and frequent itemsets using specialized data structures. Data mining is closely tied to Big Data Research, where the volume, variety, and velocity of data demand scalable storage and computing. Its applications are broad: detecting fraud and misinformation, screening biomedical and bioinformatic data to link genes and proteins to disease, supporting diagnosis, and informing prediction and decision-making across science, medicine, and industry. Effective data mining couples appropriate algorithms with careful data preparation and validation to ensure that discovered patterns are reliable rather than spurious. As a discipline it underpins knowledge discovery, turning ever-growing data collections into insight that guides understanding and action.

Research published in this journal

10 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 10 articles above have been cited 57 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

• SPADE-LSTM: An Integrated Sequential Pattern Mining and Deep Learning for Badminton Next-Stroke Prediction
  2026 · International Journal of Advances in Data and Information Systems
• An Architectural Optimization Framework for Scalable Spatial Clustering in High-Redundancy Environments
  2026 · Technologies
• Efficient Closed Frequent Itemset Mining: Enhancing Association Rule Discovery with Vertical Format Algorithms
  A. A. Ali et al. · 2025 · 2025 International Conference on Frontier Technologies and Solutions (ICFTS)
• Improving T2D machine learning-based prediction accuracy with SNPs and younger age
  2025 · Computational and Structural Biotechnology Journal
• Investigation of Aldose Reductase Inhibitors: Implications for Therapeutic Targets in Diabetic Retinopathy
  2025 · Journal of Molecular Structure
• GlassBoost: A Lightweight and Explainable Classification Framework for Tabular Datasets
  2025 · Applied Sciences
• Clustering of the Air Pollution Standard Index (ISPU) in the Province of DKI Jakarta Using the CLARANS Algorithm
  A. Azzahra et al. · 2025 · JOURNAL OF APPLIED INFORMATICS AND COMPUTING
• Gradient Descent Decision Tree Algorithm and Nonlinear Programming for Credit Risk Assessment and Credit Strategy
  Guoqing Chen et al. · 2025 · Emerging Science Journal

A sample of recent works citing this journal's research on Data Mining, linking to each citing work.

This page summarises published research for orientation; it is not medical or professional advice.

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