Probabilistic Clustering with EM algorithm: Algorithm and Visualization with Julia from scratch

Abstract

On this article, I'll write probabilistic clustering by EM algorithm from scratch with Julia. Here, I'll touch only about mixture of Gaussian case.
The outcome of clustering becomes below. To the simple artificial data, it is working.

Introduction to K-medoids: Algorithm and Visualization with Julia from scratch

Abstract

On this article, I'll write K-medoids with Julia from scratch.
Although K-medoids is not so popular algorithm if you compare with K-means, this is simple and strong clustering method like K-means. So, here, as an introduction, I'll show the theory of K-medoids and write it with Julia.
As a goal, I'll make animation like below.

Introduction to K-means: Algorithm and Visualization with Julia from scratch

Abstract

On this article, I'll write K-means with Julia from scratch and show animation to see how the algorithm works.
K-means is very simple unsupervised algorithm for clustering. So, when I start to study new programming language, I always use K-means as the theme for writing from scratch.
The following GIF shows how data points are classified into clusters on the way of algorithm going. Relatively easily, we can write K-means code and plot this kind of animation with Julia.



On this article, I used Julia with version 0.6.2.

Note for speeding up Julia's code

Abstract

On this article, I'll re-write the Julia code, which I wrote before, for speed up.
One of the huge advantages of Julia is its performance, speed. But to make advantage of it, we need to write the code in proper rule. So, here, I’ll re-write the code to more efficient one.

kNN by Julia from scratch

Abstract

On this article, I'll write naive kNN algorithm with Julia. Recently, I started to use Julia and need to practice. kNN is relatively simple algorithm and nice for practice. So, here, I'll write simple kNN with Julia.