Low-Rank matrix factorization and co-clustering algorithms for analyzing large data sets

Authors:
Archana Donavalli;Manjeet Rege;Xumin Liu;Kourosh Jafari-Khouzani
Affiliations:
Department of Computer Science, Rochester Institute of Technology, Rochester, NY;Department of Computer Science, Rochester Institute of Technology, Rochester, NY;Department of Computer Science, Rochester Institute of Technology, Rochester, NY;Department of Radiology Research, Henry Ford Hospital, Detroit, Michigan
Venue:
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
Year:
2010

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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.