From the course: Data Science Tools of the Trade: First Steps
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Weka: Clustering
From the course: Data Science Tools of the Trade: First Steps
Weka: Clustering
- [Instructor] Clustering is another very popular machine learning or ML task. Unlike classification, it belongs to unsupervised learning. Let's find out how Weka handles this very common task of clustering in data science. The first step is loading our dataset. We'll be using the iris dataset provided by Weka by default. This is a famous dataset that contains morphologic variation of iris flowers of three related species. Since there are three different species involved here, my hope is that our clustering ML can detect the three groups as expected. Now that I've loaded the data, let's move forward. Choose the Cluster tab next. Click choose. Here you see several well known clustering algorithms. Pick SimpleKMeans. You can adjust the properties of the clustering algorithm of your choice by clicking on its name here. We can guide our clustering algorithm a little more by giving the number of clusters. Since we know the number will most probably be three, let's put three here and click…
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