Description

Today data has grown more and more all around the world in tremendous way. According to Statista, the total amount of data has grown has forecasted globally by 2021 as 79 zettabytes. Using this data, data analyst can analyse, visualize and construct the pattern based on end users requirements. For analyzing and visualization the data, here in need of more fundamental techniques for understanding types of data sets, size and frequency of data set to take proper decision. There are different types of data such as relational data base, could be data warehouse database, transactional data, multimedia data, spatial data, WWW data, time series data, heterogeneous data, text data.

There are more and more number of data mining techniques including pattern recognition, and machine learning algorithms. This book focused on data clustering technique, which is one of the sub part of machine learning.

Clustering is one of the Unsupervised Machine Learning technique used for statistical data analysis in many fields, which is one of the sub branch of data mining. There are two main sub branches such as supervised machine learning and unsupervised machine learning under data mining. All classification methods including Rule based classification, Decision Tree (DT) classification, Random forest classification, support vector machine, etc., and linear regression based learning are come under Supervised Learning. Then all clustering algorithms such as K-Means (KM), K-Harmonic Means (KHM), Fuzzy clustering, Hybrid clustering, Optimization based clustering association based mining etc., are come under unsupervised clustering. Clustering algorithms can also be categorized into different types such as, traditional clustering algorithms such as, hierarchical clustering algorithms, grid based clustering, partitioning-based clustering, density based clustering.

There are wide variety of clustering algorithms to cluster the data point into a set of disjoint classes. After clustering of the data all related data objects come under one group of data and different or dissimilar data objects come under another cluster of data. Clustering algorithms can be applied in most of the fields such as medical, engineering, financial forecasting, education, business, commerce, and so on. Clustering Algorithms can also use in Data Science to analyse more complicated problems and to get more valuable insights from the data.