Internet of Things and Big Data Technologies for Next Generation Healthcare by Chintan Bhatt Nilanjan Dey & Amira S. Ashour

Author:Chintan Bhatt, Nilanjan Dey & Amira S. Ashour
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

Importance of Unsupervised Learning Algorithms in Big data Analytics: Unsupervised learning is one of the most effective way for analyzing big data as no training set data is required here. In the big data domain, analysis is normally required on the dataset under exploration where predefined rule set is not available. So in this situation, unsupervised learning is quite effective to find useful patterns above and beyond noise.

Common Algorithms in Unsupervised learning: The following section describes the commonly used algorithms in unsupervised learning.

A. Clustering Algorithms:

Clustering is a popular concept which groups organization of unlabeled data based on similarity. So as a result, similar kind of data belongs to one group and other reside in another group. There are mainly three types of clustering algorithms are available, out of which K-means is the most widely used technique [29]. Bayesian Algorithms: The major goal of this kind of algorithm is to generate a posteriori distribution over the collection of all partitions of the data.

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