Scholars' Press Stream Clustering: Unsupervised Machine Learning

Recently as applications produce overwhelming data streams, the need for strategies to analyze and cluster streaming data becomes an urgent and crucial research area for knowledge discovery. The main objective and the key aim of data stream clustering are to gain insights into incoming data. Recognizing all probable patterns in this boundless data, which arrives at varying speeds and structure and evolves over time, is very important in this analysis process. The point is to find the arbitrary shaped clusters in just a single pass clustering while keeping up with different arrival speeds of these data streams in limited time and limited memory, in addition to considering evolving data over time and handling outliers. The target is considering all these challenges without the need for much or some parameter information for data processing.This book discussed theatrically and practically the most remarkable stream clustering algorithms and how each one handling the aforementioned challenges, which benefits both in the academic and the industrial domains.