Nsupervised Learning Techniques Cluster Analysis Examples With Matlab

UNSUPERVISED LEARNING TECHNIQUES: CLUSTER ANALYSIS. EXAMPLES WITH MATLAB

Data Science Algorithms for Unsupervised Learning

Artificial Intelligence and Data Science combines mathematical algorithms and techniques from Machine Learning, Deep Learning and Big Data to extract the knowledge contained in the data and present it in an understandable and automatic way. In the field of Artificial Intelligence and Data Science, we can highlight two types of learning that are widely used to train machines and devices to understand a set of data: supervised learning and unsupervised learning. supervised learnig techniques trains a model on known input and output data so that it can predict future outputs, and unsupervised learning techniques finds hidden patterns or intrinsic structures in input data. Unsupervised learning is more closely aligned with Artificial Intelligence as it gives the idea that a machine can learn to identify complex processes and patterns without the need for a human to provide guidance and supervision throughout the learning process. This book develops unsupervised learning techniques including cluster analysis, hierarchical cluster analysis, nonhierarchical cluster analysis, clustering with gaussian mixture models, clustering with hidden Markov models, Markov chaines, nearest neighbors classifiers, kNN classifiers, cluster visualization and cluster evaluation

Machine Learning and Clustering for a Sustainable Future

This book explores cutting-edge machine learning and clustering techniques to tackle critical challenges in engineering, environmental science, and sustainability. The book provides an in-depth examination of clustering methodologies, covering unsupervised and supervised techniques, data preprocessing, distance metrics, and cluster validation methods such as the elbow and silhouette techniques. Readers will find practical insights into applying these methods to real-world problems, including clustering greenhouse gas emissions, optimizing energy systems, and analyzing the energy-food nexus in the context of global crises. By integrating theoretical foundations with hands-on applications, this book serves as a valuable resource for researchers, engineers, and professionals seeking data-driven solutions for sustainability challenges.