Aynadis Temesgen Gebru
BIG DATA AND CLUSTERING QUALITY INDEX COMPUTATION.
Rel. Paolo Garza, Tania Cerquitelli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2019
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Abstract
Clustering analysis is unsupervised machine learning technique that partitions a dataset multiple groups or clusters so that instances in the cluster have high similarity but not with instances of other clusters. There exists number of methods to accomplish the process of clustering analysis. The quality of the results generated by a clustering method is measured by cluster evaluation. Some clustering methods demand the number of clusters into which data is going to be partitioned. Cluster evaluation determines the number of clusters to be used as an input to the clustering methods. A comparison between the result of the different clustering methods can also be performed using cluster evaluation.
The traditional cluster evaluation algorithms are not applicable for bigdata due to a size limitation and run time cost
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