# Tag: K-means

**Comparing Distance Measurements with Python and SciPy**- Aug 15, 2017.

This post introduces five perfectly valid ways of measuring distances between data points. We will also perform simple demonstration and comparison with Python and the SciPy library.**K-means Clustering with Tableau – Call Detail Records Example**- Jun 16, 2017.

We show how to use Tableau 10 clustering feature to create statistically-based segments that provide insights about similarities in different groups and performance of the groups when compared to each other.**Machine Learning Workflows in Python from Scratch Part 2: k-means Clustering**- Jun 7, 2017.

The second post in this series of tutorials for implementing machine learning workflows in Python from scratch covers implementing the k-means clustering algorithm.**K-means Clustering with R: Call Detail Record Analysis**- Jun 6, 2017.

Call Detail Record (CDR) is the information captured by the telecom companies during Call, SMS, and Internet activity of a customer. This information provides greater insights about the customer’s needs when used with customer demographics.**KDnuggets™ News 17:n10, Mar 15: Becoming a Data Science Unicorn; What Makes a Good Data Visualization?**- Mar 15, 2017.

6 Business Concepts you need to become a Data Science Unicorn; What Makes a Good Data Visualization?; Best Data Science Courses from Udemy (only $19 till Mar 31); K-Means & Other Clustering Algorithms: A Quick Intro with Python; Free Online Data Science & Big Data Books**Toward Increased k-means Clustering Efficiency with the Naive Sharding Centroid Initialization Method**- Mar 13, 2017.

What if a simple, deterministic approach which did not rely on randomization could be used for centroid initialization? Naive sharding is such a method, and its time-saving and efficient results, though preliminary, are promising.**Beginner’s Guide to Customer Segmentation**- Mar 9, 2017.

At the core of customer segmentation is being able to identify different types of customers and then figure out ways to find more of those individuals so you can... you guessed it, get more customers!**K-Means & Other Clustering Algorithms: A Quick Intro with Python**- Mar 8, 2017.

In this intro cluster analysis tutorial, we'll check out a few algorithms in Python so you can get a basic understanding of the fundamentals of clustering on a real dataset.**Automatically Segmenting Data With Clustering**- Feb 9, 2017.

In this post, we’ll walk through one such algorithm called K-Means Clustering, how to measure its efficacy, and how to choose the sets of segments you generate.**Top KDnuggets tweets, Feb 01-07: Learning to Learn by Gradient Descent by Gradient Descent**- Feb 8, 2017.

Also #DeepLearning Research Review: Natural Language Processing; K-Means, Other Clustering Algorithms: A Quick Intro with #Python; Why #DeepLearning Needs Assembler Hackers.**Introduction to K-means Clustering: A Tutorial**- Dec 9, 2016.

A beginner introduction to the widely-used K-means clustering algorithm, using a delivery fleet data example in Python.**Clustering Key Terms, Explained**- Oct 18, 2016.

Getting started with Data Science or need a refresher? Clustering is among the most used tools of Data Scientists. Check out these 10 Clustering-related terms and their concise definitions.**Comparing Clustering Techniques: A Concise Technical Overview**- Sep 26, 2016.

A wide array of clustering techniques are in use today. Given the widespread use of clustering in everyday data mining, this post provides a concise technical overview of 2 such exemplar techniques.**Top 10 Data Mining Algorithms, Explained**- May 21, 2015.

Top 10 data mining algorithms, selected by top researchers, are explained here, including what do they do, the intuition behind the algorithm, available implementations of the algorithms, why use them, and interesting applications.**Data Science 102: K-means clustering is not a free lunch**- Jan 29, 2015.

K-means is a widely used method in cluster analysis, but what are its underlying assumptions and drawbacks? We examine what happens for non-spherical data and unevenly sized clusters.