Agglomerative clustering is a hierarchical clustering method of clustering data points into clusters based on their similarity. In this article, we will discuss how to implement Agglomerative Clustering in Python Using the sklearn module. What Is Agglomerative Clustering? It is a bottom-up approach hierarchical clustering approach, in which each data point is initially considered as…
Dendrograms are a great tool to visualize hierarchy in a dataset. In this article, we will discuss what a dendrogram is, and how to plot a dendrogram in python. This article also discusses the advantages, disadvantages, and applications of dendrograms. Dendrogram Definition A dendrogram is a graphical representation of a hierarchical structure, such as a…
Clustering algorithms play an important role in tasks like customer segmentation. In this article, we will discuss agglomerative clustering with a numerical example. We will also discuss its advantages, disadvantages, and applications. What is Agglomerative Clustering? Agglomerative clustering is an unsupervised machine-learning algorithm that groups data into clusters. It is a bottom-up approach hierarchical clustering…
Hierarchical clustering is an unsupervised machine-learning algorithm used to group data points into clusters. In this article, we will discuss the basics of hierarchical clustering, its advantages, disadvantages, and applications in real-life situations. What is Hierarchical Clustering? Hierarchical clustering is an unsupervised machine learning algorithm used to group data points into various clusters based on…
The K-Modes and K-Prototypes clustering algorithms are partitioning clustering algorithms. We need to specify the required number of clusters while clustering datasets using these algorithms. However, we don’t know the optimal number of clusters beforehand. For this, we can use the silhouette coefficient approach. In this article, we will discuss the Silhouette Coefficient approach for…
Deciding the optimal number of clusters while clustering datasets using partition-based clustering algorithms is essential. In this article, we will discuss the silhouette coefficient in python to decide the optimal number of clusters in k-means clustering. What is Silhouette Coefficient? The silhouette coefficient is a measure of cohesion between the data points in the clusters…
Partitioning-based clustering algorithms have a major issue. While implementing these algorithms, we don’t know the exact number of clusters to be formed. In this article, we will discuss the elbow method to find the optimal number of clusters in k-means and k-modes clustering algorithms. We will also implement the entire procedure of finding optimal clusters…
In partition-based clustering algorithms, we face a major challenge in deciding the optimal number of clusters. In this article, we will discuss how we can find the best k in k-prototypes clustering using the elbow method while clustering mixed data types in Python. What Is the K-Prototypes Clustering Algorithm? The k-prototypes clustering algorithm is a…
Clustering datasets into different groups finds its applicability in many industries. K-Means clustering is one of the most popular clustering algorithms. However, it only works on numerical data. In the real world, any dataset contains numeric as well as categorical attributes. In such cases, we can use k-prototypes clustering. In this article, we will discuss…
Clustering algorithms are unsupervised machine learning algorithms used to segment data into various clusters. In this article, we will discuss the K-Prototypes clustering algorithm with a numerical example. We will also discuss the advantages and disadvantages of the k-prototypes clustering algorithm. What is K-Prototypes clustering? K-Prototypes clustering is a partitioning clustering algorithm. We use k-prototypes…