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Hybrid Clustering Technique to Cluster Big Data in the Hadoop Ecosystem: Big Data Application
Abstract
Big data analytics as well as data mining play vital roles in extracting the hidden statistics. Customary advances for investigation and extraction of hidden information from data may not exert efficiently for big data because of its complex, elevated volume nature. Data clustering is a data mining technique that exacts the useful data from the data by grouping data into clusters. In big data as the data is complex and of very large volume, individual clustering techniques may not consider all the samples, which may lead to inaccurate results. To overcome this inaccuracy, the proposed method is the combination of dynamic k-means and hierarchical clustering algorithms. This proposed method can be called a hybrid method. Being a hybrid method will overcome a few drawbacks like static k value. In this chapter, the proposed method is compared with existing algorithms by using some clustering metrics.
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