ARTIFICIAL INTELLIGENCE TOOLS: CLUSTER ANALYSIS AND KNN CLASSIFIERS. Examples with MATLAB

Artificial Intelligence 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. Neural networks and their applications are a fundamental tool to develop work in Artificial Intelligence. The availability of large volumes of data and the generalized use of computer tools has transformed research and data analysis, orienting it towards certain specialized techniques encompassed under the generic name of Analytics that includes Multivariate Data Analysis (MDA), Data Mining, Machine Learning and other Business Intelligence techniques. Data Mining and Machine Learning uses two types of techniques: predictive techniques (supervised learnig techniques) , which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques (unsupervised learning techniques), which finds hidden patterns or intrinsic structures in input data. Descriptive techniques finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses. Clustering is the most common descriptive technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition. This book develops classification descriptive techniques. Among the most interesting models are the neural networks. MATLAB implementrs the Neural Network Toolbox specialized in the techniques of analytics based on neural networks. Throughout this book the techniques of analytics for clustering and classification based on neural networks are developed using MATLAB software and in concrete Neural Network Toolbox.