Book Image

Python Data Mining Quick Start Guide

By : Nathan Greeneltch
Book Image

Python Data Mining Quick Start Guide

By: Nathan Greeneltch

Overview of this book

Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle.
Table of Contents (9 chapters)

Clustering methods

The clustering methods in scikit-learn have a nice congruent usage that, for the most part, matches the following pseudocode across all the algorithms:

### this is pseudocode. it will not execute ###
# import module and instantiate method object
from sklearn.cluster import Method
clus = Method(args*)

# fit to input data
clus.fit(X_input)

# get cluster assignments of X_input
X_assigned = clus.labels_

The rest of this chapter will cover some common methods used for data clustering. The following is a group of plots comparing different cluster methods and how they assign data points into groups:

Take a minute to study the preceding "Comparing Cluster Methods" screenshot and look for any qualitative trends or patterns before reading the following sections. Your goal should be to read the rest of the chapter looking for validation of your qualitative pattern...