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Document clustering is a traditional, efficient and yet quite effective, textmining technique when we need to get a better insight of the documents of acollection that could be grouped together. The K-Means algorithm and theHierarchical Agglomerative Clustering (HAC) algorithm are two of the most knownand commonly used clustering algorithms; the former due to its low time costand the latter due to its accuracy. However, even the use of K-Means in textclustering over large-scale collections can lead to unacceptable time costs. Inthis paper we first address some of the most valuable approaches for documentclustering over such 'big data' (large-scale) collections. We then present twovery promising alternatives: (a) a variation of an existing K-Means-based fastclustering technique (known as BigKClustering - BKC) so that it can be appliedin document clustering, and (b) a hybrid clustering approach based on acustomized version of the Buckshot algorithm, which first applies ahierarchical clustering procedure on a sample of the input dataset and then ituses the results as the initial centers for a K-Means based assignment of therest of the documents, with very few iterations. We also give highly efficientadaptations of the proposed techniques in the MapReduce model which are thenexperimentally tested using Apache Hadoop and Spark over a real clusterenvironment. As it comes out of the experiments, they both lead to acceptableclustering quality as well as to significant time improvements (compared toK-Means - especially the Buckshot-based algorithm), thus constituting verypromising alternatives for big document collections.

Efficient Big Text Data Clustering Algorithms using Hadoop and Spark