Lab 6 Description - MadhuriGumma/Python-Programming GitHub Wiki

Spectral Clustering: In this example, an image with connected circles is generated and spectral clustering is used to separate the circles.

In these settings, the :ref:spectral_clustering approach solves the problem know as 'normalized graph cuts': the image is seen as a graph of connected voxels, and the spectral clustering algorithm amounts to choosing graph cuts defining regions while minimizing the ratio of the gradient along the cut, and the volume of the region.

As the algorithm tries to balance the volume (ie balance the region sizes), if we take circles with different sizes, the segmentation fails.

In addition, as there is no useful information in the intensity of the image, or its gradient, we choose to perform the spectral clustering on a graph that is only weakly informed by the gradient. This is close to performing a Voronoi partition of the graph.

In addition, we use the mask of the objects to restrict the graph to the outline of the objects. In this example, we are interested in separating the objects one from the other, and not from the background.

Linear Regression: I have used 'google.csv' to plot linear regression graph using matplotlib.pyplot, sklearn

T-Shirt size clustering:

For this I have used vstack and array also I have used scipy.cluster.vq using k-means clustering which keeps on checking for the nearest centroid until the points become fixed. I have created 3 centroids for 3 sizes of T-shirt i.e., 'small','medium','Large'