Hello, I am
Juliano Sena da Silva Carlos
I’m Software Engineer, always trying to do my best.
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Assumptions of K-Means Clustering – Part 1
K-Means Clustering is a popular unsupervised machine learning algorithm that groups data into a predefined number of clusters based on similarity. However, like every algorithm, K-Means makes certain assumptions about the data and the structure…
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Beyond K-Means: Other Notions of Distance
K-Means clustering is a great starting point for understanding unsupervised machine learning. However, it comes with important limitations, especially when data is complex or doesn’t conform to its assumptions. In this article, we explore what…
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Examples of K-Means Clustering Problems
K-Means clustering is one of the simplest and most widely used unsupervised machine learning algorithms. Its goal is to group data points into clusters based on similarity. In this article, we’ll explore different types of…
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Understanding K-Means Clustering and Its Alternatives
K-Means clustering is a popular method for grouping data based on similarity. However, it has limitations, especially when determining the optimal number of clusters. This post explores these limitations and introduces alternative methods, including hierarchical…
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Mastering Modern Software Architecture: A Comprehensive Guide to Essential Design Patterns
In the fast-paced world of software development, understanding foundational architectural concepts is paramount. This guide provides a comprehensive overview of essential design patterns, offering a stepping stone into the complex yet fascinating realm of solution…
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Making Sense of Unstructured Data: How to Evaluate Clustering?
Clustering is one of the most popular techniques for exploring and organizing unstructured data—like text, images, or customer behavior. But once you apply a clustering algorithm, how do you know if it actually worked well?…