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This book is an attempt to present up to the point material on Hierarchical Clustering algorithms in a precise manner. It is primarily meant as an elementary material for a student who begins to do a course on Machine Learning. The material begins with an introduction to data types and data metrics. Then it proceeds to cover some most popular techniques in agglomerative and divisive clustering approaches. In addition to the theory, some code snippets written using R and Python, towards implementation of the techniques learned in this book are also given. Towards the end, a case study is also included so that the reader can have a clear understanding and practical utility of the subject.