Machine Learning with Matlab. Unsupervised Learning Techniques : Classification

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), Machine Learning, Data Mining and other Business Intelligence techniques. Machine learning uses two types of techniques: Supervised Learning techniques (predictive techniques), which trains a model on known input and output data so that it can predict future outputs, and Supervised Learning techniques (descriptive techniques), which finds hidden patterns or intrinsic structures in input data. Unsupervised learning 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 unsupervised learning techniques