A Python implementation of divisive and hierarchical clustering algorithms. The algorithms were tested on the Human Gene DNA Sequence dataset and dendrograms were plotted.

Hierarchical divisive clustering algorithm execution, visualization and Interactive visualization.

In Divisive we have all points in one cluster initially and we break the cluster into required number of clusters.

Unsupervised Learning Projects – Research-oriented Python portfolio exploring clustering, customer segmentation, pattern discovery, and evaluation on real-world datasets using KMeans, DBSCAN, Agglomerative, and divisive methods.

Supervised and unsupervised learning algorithms using sclearn package

Data visualization and implementation of clustering algorithms on a dataset of football players

Performed KMeans, Agglomerative, Divisive, DBSCAN clustering on FIFA dataset along with outlier detection and cluster analysis

Comparing different clustering algorithms

First steps in clustering with k-Means and hierarchical clustering.

You will learn to use hierarchical clustering to build stronger groupings which make more logical sense. This course teaches you how to build a hierarchy, apply linkage criteria, and implement hierarchical clustering

HG4TS_SSF: Hierarchy Generation for Time Series using Stochastic Splitting Functions