[PDF] Practical Guide To Cluster Analysis In R Unsupervised Machine

What are the main parts of the book clustering in R?

The main parts of the book include: •distance measures, •partitioning clustering, •hierarchical clustering, •cluster validation methods, as well as, •advanced clustering methodssuch as fuzzy clustering, density-based clustering and model-based clustering. The book presents the basic principles of these tasks and provide many examples in R.

Are there any good books on unsupervised machine learning/clustering?

Although there are several good books on unsupervised machine learning/clustering and related topics, we felt that many of them are either too high-level, theoretical or too advanced. Our goal was to write a practical guide to cluster analysis, elegant visualization and interpretation.

What is cluster analysis?

Cluster analysis is popular in many ?elds, including: • Incancer researchfor classifying patients into subgroups according their gene expression pro?le. This can be useful for identifying the molecular pro?le of patients with good or bad prognostic, as well as for understanding the disease.

How to assess the clustering tendency before applying any clustering algorithm?

Before applying any clustering algorithm to a data set, the ?rst thing to do is to assess theclustering tendency. That is, whether applying clustering is suitable for the data. If yes, then how many clusters are there.