How to Build Audience Clusters With Website Data Using BigQuery ML

Building customer profiles is now easier than ever with BigQuery ML, using a technique called clustering. In this post, you’ll learn how to create segmentation and how to use these audiences for marketing activation.

PictureText: Interactive Visuals of Text

PictureText: Interactive Visuals of Text. Solving this would be a tremendous step forward in how we consume information and I will definitely NOT be able to solve it by the end of this article. My aim is, however, to propose an approach for a tiny step forward.

Cheat sheet for implementing 7 methods for selecting the optimal number

Select the optimal number of clusters based on multiple clustering validation metrics like Gap Statistic, Silhouette Coefficient, Calinski-Harabasz Index etc.

UMAP and K-Means to Classify Characters [League of Legends]

UMAP and K-Means to Classify Characters — and why it’s useful (League of Legends). Using Dimensionality Reduction and Clustering Algorithms to segment League of Legends “Champions” into Classes.

How to Manage Kubernetes Secrets Securely in Git

We’ll use Sealed Secret, designed to easily fit into automated workflows like GitOps. In this article, we will install the Sealed Secrets operator and demonstrate how to use it. How to Manage Kubernetes Secrets Securely in Git

Getting Started with GitOps with Flux and Gitlab

Learn how to use the GitOps methodology to simplify your Kubernetes deployments. In this guide we will set up Flux and deploy a demo application via the Git repository.

The step-by-step approach using K-Means Clustering using SAS

Here, in this article, I am trying to explain the K-Means clustering algorithm from scratch and the implementation of this using SAS.

Handling Outliers in Clusters using Silhouette Analysis

Identify and remove outliers in each clusters from K-Means clustering. This article will cover how to handle outliers after clustering data into several clusters using Silhouette Analysis.

Visualizing the Density Based Clustering Algorithms

We talk of yet another family of clustering algorithms. Density based clustering algorithms are ones that proceed by finding the areas with a higher concentration of data points and merge those with similar concentration into a single cluster.

Customer Segmentation with RFM Analysis

Customer Segmentation with RFM Analysis. Finally a clustering model you won’t have to spending time explaining.

Silhouette Method — Better than Elbow Method to find Optimal Clusters

Deep dive analysis of Silhouette Method to find optimal clusters in k-Means clustering. In this article we will cover two such methods: Elbow Method; Silhouette Method

Clustering Moving Object Trajectories

Clustering Moving Object Trajectories. How many different trajectories are there between two endpoints?

Hierarchical Clustering of Countries Based on Eurovision Votes

Hierarchical Clustering of Countries Based on Eurovision Votes. A walk-through example of how you can apply hierarchical clustering on Eurovision Votes

A Brief Introduction to Self-Organizing Maps

Self-Organizing Maps for Dimension Reduction, Data Visualization, and Clustering. In this post, I share my understanding of SOM, how it learns, methods, and limitations of the SOM.

Beer Here or There?

In this project, we aimed to identify one or more optimal locations to open a new brewery in the twin cities, Minneapolis and St. Paul, Minnesota. As there already exists a vibrant community of small, independent breweries in the area, we looked for locations that do not already have breweries nearby.

Unsupervised on the Streets of New York

Unsupervised on the Streets of New York. Taking a Deeper Look at Gentrifying Census Tracts with Cluster Classification

How to cluster images based on visual similarity

Use a pre-trained neural network for feature extraction and cluster images using K-means. In this tutorial, I'm going to walk you through using a pre-trained neural network to extract a feature vector from images and cluster the images based on how similar the feature vectors are.

Anomaly Detection is in the Eye of the Beholder

Anomaly Detection is in the Eye of the Beholder. What a deck of playing cards reveals about detecting outliers. With proper fitting, a supervised machine learning algorithm may even be able to find some novel attacks.

3 minute read to ‘How to find optimal number of clusters using K-means Algorithm’

Basically it is the sum of squared distance (usually Euclidean distance) from it's nearest centroid (center point of cluster). It decreases with increasing number of clusters(k). Aim is to find the bend (like an elbow joint) point in the graph.

Gaussian Mixture Models vs K-Means. Which One to Choose?

In this article, we will see that both models offer a different performance in terms of speed and robustness. We will also see that it is possible to use K-Means as an initializer for GMs which tends to boost the performance of the clustering model.