In this chapter, we will cover the clustering-based learning methods, and in specific the k-means clustering algorithm among others. Clustering-based learning is an unsupervised learning technique and thus works without a concrete definition of the target attribute. You will learn basics and the advanced concepts of this technique, and get hands-on implementation guidance in using Apache Mahout, R, Julia, Apache Spark, and Python to implement the k-means clustering algorithm.
The following figure depicts different learning models covered in this book, and the techniques highlighted in orange will be dealt in detail in this chapter:
The topics listed next are covered in depth in this chapter:
The core principles and objectives of the clustering-based learning methods
How to represent clusters and understand the required distance measurement techniques
Learning in depth, the k-means clustering and choosing the right clustering algorithm and the rules of cluster...