Machine learning with Python and sklearn – Hierarchical Clustering (E-commerce dataset example)
In this Machine Learning & Python video tutorial I demonstrate Hierarchical Clustering method.
Hierarchical Clustering is a part of Machine Learning and belongs to Clustering family:
– Connectivity-based clustering (hierarchical clustering)
– Centroid-based clustering (K-Means Clustering) – https://www.youtube.com/watch?v=iybATqk6LNI
– Distribution-based clustering
– Density-based clustering
In data mining and statistics, Hierarchical Clustering also called hierarchical cluster analysis or HCA is a method of cluster analysis which seeks to build a hierarchy of clusters. In this video I demonstrate how Agglomerative Hierarchical Clustering is working.
Must know for Hierarchical Clustering is knowing Dendrograms. Dendrogram helps you to decide the optimal number of clusters for your dataset.
For executing task in Python I used:
– sklearn library that is for Machine Learning algorithms.
– ward method that means Minimum Variance Method.
If you are interesting more in Hierarchical Clustering, read my article on LinkedIn where I described my experiment about combining Machine Learning (Hierarchical Clustering) in GIS (Geographical Information System). – https://www.linkedin.com/pulse/machine-learning-gis-hierarchical-clustering-urban-bielinskas
Data-set for this example is taken from https://www.kaggle.com. There you can find many dataset for very different Machine Learning tasks.
Hierarchicaal Clustering is very usable in solving Data Analysis, Data Mining and Statistics problems.
If you have any question or comments please write below.
Do not forget to subscribe me if want to follow my new videos about Machine Learning, Data Science, Python programming and relative issues.