An overview of clustering algorithms, including centroid-based (K-Means, K-Means++), density-based (DBSCAN), hierarchical, and distribution-based clustering. The article explains how each type works, its pros and cons, provides code examples, and discusses use cases.
A simple and intuitive explanation of DBSCAN (Density-Based Spatial Clustering of Applications with Noise), a clustering algorithm that can identify outliers, extract new features, compress data, and perform novelty detection. The article provides a fast implementation of DBSCAN in Python.
This article introduces interpretable clustering, a field that aims to provide insights into the characteristics of clusters formed by clustering algorithms. It discusses the limitations of traditional clustering methods and highlights the benefits of interpretable clustering in understanding data patterns.
Discusses reasons why clustering in data science might not produce desired results and how to address these issues.
This article discusses a method for automatically curating high-quality datasets for self-supervised pre-training of machine learning systems. The method involves successive and hierarchical applications of k-means on a large and diverse data repository to obtain clusters that distribute uniformly among data concepts, followed by a hierarchical, balanced sampling step from these clusters. The experiments on three different data domains show that features trained on the automatically curated datasets outperform those trained on uncurated data while being on par or better than ones trained on manually curated data.
Unlock advanced customer segmentation techniques using LLMs, and improve your clustering models with advanced techniques
Elbow curve and Silhouette plots both are very useful techniques for finding the optimal K for K-means clustering
