Clustering Application in Face Recognition in Python

We used face datasets for PCA application here: https://charleshsliao.wordpress.com/2017/05/28/preprocess-pca-application-in-python/ It also will be interesting to see how clustering algorithms assign images into different clusters and visualize them. We use the data from sklearn library(need to download face datasets separately), and the IDE is sublime text3. Most of the code comes from the book: https://www.goodreads.com/book/show/32439431-introduction-to-machine-learning-with-python?from_search=true

Clustering Algorithms Evaluation in Python

Sometimes we conduct clustering to match the clusters with the true labels of the dataset. Apparently this is one method to evaluate clustering results. We can also use other methods to complete the task with or without ground truth of the data. We use the data from sklearn library, and the IDE is sublime text3.… Continue reading Clustering Algorithms Evaluation in Python

DBSCAN in Python

Another very useful clustering algorithm is DBSCAN (which stands for “Density- based spatial clustering of applications with noise”). The main benefits of DBSCAN are that ###a) it does not require the user to set the number of clusters a priori, ###b) it can capture clusters of complex shapes, and ###c) it can identify point that… Continue reading DBSCAN in Python

Interpretation Kmeans/DBSCAN, with SNS example and NAs

This dataset was compiled by Brett Lantz while conducting sociological research on the teenage identities at the University of Notre Dame. Data source: Machine.Learning.with.R.2nd.Edition, Chapter 9. Dbscan Defines density as the number of points within a specified radius, There is no way to exclude outlier samples Given a specified number of neighboring samples (MinPts) within… Continue reading Interpretation Kmeans/DBSCAN, with SNS example and NAs

K-means, Hierarchical, and Feature Selection Methods

We will use the well-known iris data set to make some quick clustering. (site : http://archive.ics.uci.edu/ml/datasets/Iris) (data : http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data ) (description : http://archive.ics.uci.edu/ml/machine-learning- databases/iris/iris.names). Hierarchical Clustering with basic functions and adjust distance methods accordingly, since K-means function would not allow user to adjust distance attributes: Now comes the interesting part. We know that only some… Continue reading K-means, Hierarchical, and Feature Selection Methods