Today, we're turning our focus towards comparing various unsupervised learning methods. Our comparative study will include K-means, DBSCAN, Principal Component Analysis (PCA), Independent Component Analysis (ICA), and t-SNE.
Utilizing the Iris flower dataset, we will employ Python's scikit-learn library. Each of these methods possesses unique attributes, so understanding their comparative performance would enable us to choose the one best suited for any given scenario. Let's get started!
In our exploration of unsupervised learning, we've familiarized ourselves with a variety of clustering and dimensionality reduction techniques. Although these techniques share the primary aim of discovering the underlying data structure, the methodologies they use to achieve this can vary significantly. That's where the need for comparison arises, as it helps us select the most suitable technique for a specific problem.
Several metrics, such as accuracy, simplicity, computational efficiency, and interpretability, enable us to compare these techniques. In the following sections, we'll compare clustering and dimension reduction methods using these metrics.
Let's begin by refreshing our memory on the properties of our clustering techniques. K-means is a partition-based technique. It partitions observations into clusters in such a way that each observation belongs to the cluster with the nearest mean. The clusters formed by K-means tend to be spherical, which suits well-spaced, round clusters. However, it doesn't handle noise and outliers effectively and struggles with non-spherical and similarly sized clusters.
