Skip to main content

Classification and Clustering

In order to write a tutorial about classification, it was necessary to find an example that was broad enough that it would need to be sub-divided. Since I actually care about whether you remember this stuff, it needed to be something that a lot of people like and would relate to. And since I have a lot of international subscribers, it needed to be cross-cultural as well. So what is universal, cross-cultural, and dearly loved?
Beer.
Beer. Heck yeah.
There’s American beer, Mexican beer, German beer, Belgian beer….hell, even the Japanese make beer. There’s IPA, Lager, Pilsner. Dark, light, stout. There are so many ways to classify beer that we could spend weeks doing it (so naturally, I did).
Now, before you can classify anything you have to determine the characteristics that you’re going to use. For beer you could use country of origin, color, alcohol content, type of hops, type of yeast, and calorie count among other things. That way you could sort based on any of those characteristics to judge similarities between the various brews.
And just like that, you’ve done classification. Simple, right?
To take the example further, let’s assume that my favorite beer is Sweetwater “Take Two” (a pilsner made here in Atlanta) but I’m in Santiago, Chile this week for a conference.  The Chileans are a lovely people, but the management at my hotel doesn’t know about the wonderful goodness made by Sweetwater and they don’t have it at the lobby bar. I explain my predicament (read: “impending crisis”) to the bartender. What would a good bartender do?
If he’s been in the business for any length of time, he’s already gone through the classification step for beers but probably didn’t realize it. He has them sorted by characteristics in his head. He starts asking me questions about Take Two: “How dark is it?”, “How ‘hoppy’ does it taste?”, and “How many can you drink before passing out?”. Based on my answers he knows that what I’m describing is basically a golden-blonde pilsner with spicy hops and an earthy tone.
He might also figure out that I’ll need help back to my room at the end of the night because of all this “field research”.
So now that he has the characteristics of my favorite brew figured out, he compares that against the beers he knows. The ones with the most matching criteria form a “cluster” that he can make recommendations (and hopefully free samples) from. My night is saved, and his tip is big. Everyone is happy.
And just like that, you understand clustering.
How does this apply to the business world? There are many potential applications of classification and clustering, but a common one is identifying the characteristics of a company’s best customers and then searching a pool of potential customers for ones that meet those characteristics. If your best customers have between 1000-2500 employees, are in the manufacturing and retail verticals, and are located in the New England area of the US, that’s good information to know.
What applications can you think of?

Comments

Post a Comment

Popular posts from this blog

Common Sense Identification of the Security Problems

Organizations make key information security mistakes, which leads to inefficient and ineffective control environment. High profile data breaches and cyber-attacks drive the industry to look for more comprehensive protection measures since many organizations feel that their capability to withstand persistent targeted attacks is minimal. But at the same time, these organizations make some key information security mistakes, that jeopardize their efforts towards control robustness. Although many firms invest in security technologies and people, no one has the confidence that the measures taken are good enough to protect their data from compromises. Below are the 10 worst mistakes which are common to find, and important to address in the path of mature information security posture. If you analyze the cyber security scenarios, and organizational capabilities, the prevailing trend is a vendor-driven approach. In many cases, security professionals adopt the attitude of procuring...
Post a Comment
Read more

Real-Time Talk: Windows 10 IoT Core Background Tasks and ASP.NET Core Web Apps

Display useful information from your Windows 10 IoT Core application in an ASP.NET Core web app, essential for integrating IoT data into a solution. Windows 10 IoT background task talk with a web application using WebSockets. Problems As my path to this solution has been troublesome, I am listing here the main problems I faced so my dear readers have a better idea of dead-end streets along the way: I was not able to make the ASP.NET Core web application run under a Windows 10 IoT background service. I found no information about when or if it will be supported in the near future. ASP.NET MVC and ASP.NET Core have different SignalR implementations. I was not able to make a SignalR client for .NET Core work with SignalR hosted on a web application. I was able to make things work by directly using a WebSocket. It’s not as nice a solution as I had in my mind, but it works until things get better. Making the Background Task and Web Application Talk I worked out sim...
Post a Comment
Read more

Python and Parquet Performance

In Pandas, PyArrow, fastparquet, AWS Data Wrangler, PySpark and Dask. This post outlines how to use all common Python libraries to read and write Parquet format while taking advantage of  columnar storage ,  columnar compression  and  data partitioning . Used together, these three optimizations can dramatically accelerate I/O for your Python applications compared to CSV, JSON, HDF or other row-based formats. Parquet makes applications possible that are simply impossible using a text format like JSON or CSV. Introduction I have recently gotten more familiar with how to work with  Parquet  datasets across the six major tools used to read and write from Parquet in the Python ecosystem:  Pandas ,  PyArrow ,  fastparquet ,  AWS Data Wrangler ,  PySpark  and  Dask . My work of late in algorithmic trading involves switching between these tools a lot and as I said I often mix up the APIs. I use Pandas and PyArrow for in-RAM comput...
Post a Comment
Read more