Abstract
With the ever increasing data, there is a greater need for analyzing and extracting useful and meaningful information out of it. The amount of research being conducted in extracting this information is commendable. From clustering to bi and multi clustering, there are a lot of different algorithms proposed to analyze and discover the hidden patterns in data, in every which way possible. On the other hand, the size of the data sets is increasing with each passing day and hence it is becoming increasingly difficult to try and analyze all this data and find clusters in them without the algorithms being computationally prohibitive. In this study, we have tried to study both the domains and understand the development of the algorithms and how they are being used. We have compared the different algorithms to try and get a better idea of which algorithm is more suited for a particular situation.
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Donavalli, A., Rege, M., Liu, X., Jafari-Khouzani, K. (2012). Low-Rank Matrix Factorization and Co-clustering Algorithms for Analyzing Large Data Sets. In: Kannan, R., Andres, F. (eds) Data Engineering and Management. ICDEM 2010. Lecture Notes in Computer Science, vol 6411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27872-3_41
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DOI: https://doi.org/10.1007/978-3-642-27872-3_41
Publisher Name: Springer, Berlin, Heidelberg
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