Data Mining and Machine Learning

Aims:

The Data Mining and Machine Learning Section focuses on the algorithms and techniques for discovering patterns, extracting knowledge, and building predictive models from data. This Section aims to showcase research that advances the state-of-the-art in learning from data, from both a theoretical and an applied perspective. It emphasizes the entire process, from data pre-processing and feature engineering to the application of sophisticated learning algorithms to uncover hidden insights and facilitate data-driven decision-making.

Scope:

This Section covers the analytical pipeline for knowledge discovery. Key topics include the following:

• Machine Learning and Deep Learning: Supervised, unsupervised, semi-supervised, and reinforcement learning algorithms; neural network architectures.
• Data Mining: Pattern discovery, association rule learning, clustering, anomaly detection, and outlier analysis.
• Data Search and Mining: Efficient algorithms for searching and mining large-scale datasets.
• Big Data Analytics: Scalable analytics platforms and techniques for very large datasets.
• Applications of ML/DM in medicine, biology, industry, security, education, and recommendation systems.