In this chapter, we are using the well-known Breast Cancer Wisconsin dataset to perform a cluster analysis. Originally, the dataset was proposed in order to train classifiers; however, it can be very helpful for a non-trivial cluster analysis. It contains 569 records made up of 32 attributes (including the diagnosis and an identification number). All the attributes are strictly related to biological and morphological properties of the tumors, but our goal is to validate generic hypotheses considering the ground truth (benign or malignant) and the statistical properties of the dataset. Before moving on, it's important to clarify some points. The dataset is high-dimensional and the clusters are non-convex (so we cannot expect a perfect segmentation). Moreover our goal is not using a clustering algorithm to obtain the results of...
Hands-On Unsupervised Learning with Python
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Hands-On Unsupervised Learning with Python
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Overview of this book
Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.
This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.
By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.
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Current Title:
Hands-On Unsupervised Learning with Python
Table of Contents (12 chapters)
Preface
Free Chapter
Getting Started with Unsupervised Learning
Clustering Fundamentals
Advanced Clustering
Hierarchical Clustering in Action
Soft Clustering and Gaussian Mixture Models
Anomaly Detection
Dimensionality Reduction and Component Analysis
Unsupervised Neural Network Models
Generative Adversarial Networks and SOMs
Assessments
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Customer Reviews