Clustering

Table of Content: 1. Data Clustering Methods 2. Hierarchical Clustering 3. Density-based Clustering 4. Model-based Clustering 5. Centroid-based Clustering 6. Conclusion 1. Data Clustering Methods Data clustering is a powerful technique that uncovers patterns within unlabeled data, and it plays an essential role in various fields, including data analysis, pattern recognition, and machine learning. By identifying similar…

Introduction Hierarchical clustering is an alternative approach to prototype-based clustering like kmeans. An important advantage of hierarchical clustering is that it allows the plotting of dendrograms, which are visualizations of binary hierarchical clustering that can support the interpretation of the results by creating meaningful taxonomies. …

Telling the story of a pollen — Pollen analysis is often complex, especially when you want to analyze its temporal component and at the same time, its magnitude. …

What is Unsupervised Machine Learning? It is a method of machine learning where, as opposed to learning from a training dataset (as in supervised learning), the model itself discovers hidden patterns and insights in the data. Even without understanding what each group stands for, it divides people into groups based on some similarities. I won’t…

Introduction In the world of machine learning and data analysis, clustering algorithms play a crucial role in grouping similar data points together. Mean-shift clustering is one such algorithm that has gained popularity due to its ability to discover clusters without the need for prior knowledge of the number of clusters. …

TL;DR: A brief intro to the captivating world of clustering, where data sorting becomes as simple yet crucial as sorting laundry, enabling personalised customer experiences across diverse sectors. This post focuses on business understanding and use cases, while technical details will be covered in upcoming posts! Clustering, a technique used…

About SOMs Introduced by Finnish professor Teuvo Kohonen in the 1980s, Self-Organizing Maps or SOMs provide a means for lower dimensional and discretized representation, called a ‘map’, of datasets while retaining the topology of the data. The goal is to learn a map which responds in a similar way to alike input…

A Comprehensive Journey: Extract, Transform, Model, and Visualize A Step-by-Step Guide to Building a Customer Segmentation Dashboard in PowerBI using PyCaret Introduction Customer segmentation is a powerful technique that groups customers based on their similarities. …

In this installment of a 3 Part Series, we will walk step-by-step through everything you need to know to understand K-Means Clustering: Part 1: The Basics Basics: Understanding clustering, Euclidean Distances etc. Intuition: A Visual Walkthrough of K-Means in Action Part 2: Coding the Algorithm from Scratch Algorithm: Formal Overview Code Implementation: Python Implementation from Scratch Part 3: Real Life Implementation