Have you ever been asked to take a look at some data and come up empty handed? Maybe you were not familiar with the dataset, or maybe you didn't even know where to start. This may have been extremely frustrating, and even embarrassing, depending on who asked you to take care of the task.

You are not alone, and, interestingly enough, there are many times the data itself is simply too confusing to be made sense of. As you try and figure out what all those numbers in your spreadsheet mean, you're most likely mimicking what many unsupervised algorithms do when they try to find meaning in data. The reality is that many datasets in the real world don't have any rhyme or reason to them. You will be tasked with analyzing them with little background preparation. Don't fret, however – this book will prepare you so that you'll never be frustrated again when dealing with data exploration tasks.

For this book, we have developed some best-in-class...

Unsupervised learning is one of the most exciting areas of development in machine learning today. If you have explored machine learning bookwork before, you are probably familiar with the common breakout of problems in either supervised or unsupervised learning. Supervised learning encompasses the problem set of having a labeled dataset that can be used to either classify (for example, predicting smokers and non-smokers if you're looking at a lung health dataset) or fit a regression line on (for example, predicting the sale price of a home based on how many bedrooms it has). This model most closely mirrors an intuitive human approach to learning.

If you wanted to learn how to not burn your food with a basic understanding of cooking, you could build a dataset by putting your food on the burner and seeing how long it takes (input) for your food to burn (output). Eventually, as you continue to burn your food, you will build a mental...

Being able to find groups of similar data that exist in your dataset can be extremely valuable if you are trying to find its underlying meaning. If you were a store owner and you wanted to understand which customers are more valuable without a set idea of what valuable is, clustering would be a great place to start to find patterns in your data. You may have a few high-level ideas of what denotes a valuable customer, but you aren't entirely sure in the face of a large mountain of available data. Through clustering you can find commonalities among similar groups in your data. If you look more deeply at a cluster of similar people, you may learn that everyone in that group visits your website for longer periods of time than others. This can show you what the value is and also provides a clean sample size for future supervised learning experiments.

Identifying Clusters

The following figure shows two scatterplots:

Figures 1.2: Two distinct...

Hopefully, by now, you can see that finding clusters is extremely valuable in a machine learning workflow. However, how can you actually find these clusters? One of the most basic yet popular approaches is by using a cluster analysis called k-means clustering. k-means works by searching for K clusters in your data and the workflow is actually quite intuitive – we will start with the no-math introduction to k-means, followed by an implementation in Python.

No-Math k-means Walkthrough

Here is the no-math algorithm of k-means clustering:

1. Pick K centroids (K = expected distinct # of clusters).
2. Randomly place K centroids anywhere amongst your existing training data.
3. Calculate the Euclidean distance from each centroid to all the points in your training data.
4. Training data points get grouped in with their nearest centroid.
5. Amongst the data points grouped into each centroid, calculate the mean data point and move your...

In this chapter, we have explored what clustering is and why it is important in a variety of data challenges. Building upon this foundation of clustering knowledge, you implemented k-means, which is one of the simplest yet most popular methods of unsupervised learning. If you have reached this summary and can repeat what k-means does step-by-step to your fellow classmate, good job! If not, please go back and review the previous material – the content only grows in complexity from here. From here, we will be moving on to hierarchical clustering, which, in one configuration, reuses the centroid learning approach that we used in k-means. We will build upon this approach by outlining additional clustering methodologies and approaches in the next chapter.