MEAN-SHIFT CLUSTERING ALGORITHM
This is another another algorithm that is very beneficial for image processing and computer vision. Mean-shift is similar to the BIRCH method in that it finds clusters without specifying an initial number of clusters. This is a hierarchical clustering approach, but it does not scale well when dealing with huge data sets. It operates by iterating over all data points and shifting them toward the mode. In this context, the mode is a high density area of data points in a region. This is why this method is sometimes referred to as the mode-seeking algorithm. It will repeat this procedure with each data point, moving them closer to where other data points are until all data points are allocated to a cluster.
THE MAIN CODE OF MEAN-SHIFT CLUSTERING ALGORITHM
Full Code Of Implementing Mean-Shift Clustering Algorithm
The Meanshift method can be used in image processing and computer vision (Download the dataset here). Meanshift is a clustering technique that iteratively allocates datapoints to clusters by shifting points towards the mode. The mode can be defined as the highest datapoint density. It is also known as the mode-seeking algorithm for this purpose. In this article, I will attempt to identify the number of clusters in the dataset using the mean shift approach. Practicing Mean-Shift clustering algorithm:
from sklearn.cluster import MeanShift