Coding Agglomerative Clustering
All About “Clustering Method” in Machine Learning with Videos
Clustering : “Clustering” is the process of grouping similar entities together. The goal of this unsupervised machine learning technique is to find similarities in the data point and group similar data points together.
Need of clustering : To determine the intrinsic grouping in a set unlabeled data. to organize data into clusters showing the internal structure of the data. To partition the data points. To understand and extract value from large sets of structured and unstructured data.
Types of Clustering :
Hierarchical Clustering : A tree structure that has a set of nested clusters. These are of two types.
a.) Agglomerative b.)Divisive Partitioned clustering : A division of the set of data objects into non — overlapping sets or clusters such that every data objects is in just one subset. These are of two types a.) K-means b.)Fuzzy C-means.
Agglomerative Clustering : In agglomerative or bottom-up clustering method we assign each observation to its own cluster. Then, compute the similarity (e.g., distance) between each of the clusters and join the two most similar clusters. Finally, repeat steps 2 and 3 until there is only a single cluster left.