Abstract
Co-clustering with augmented matrices (CCAM) [11] is a two-way clustering algorithm that considers dyadic data (e.g., two types of objects) and other correlation data (e.g., objects and their attributes) simultaneously. CCAM was developed to outperform other state-of-the-art algorithms in certain real-world recommendation tasks [12]. However, incorporating multiple correlation data involves a heavy scalability demand. In this paper, we show how the parallel co-clustering with augmented matrices (PCCAM) algorithm can be designed on the Map-Reduce framework. The experimental work shows that the input format, the number of blocks, and the number of reducers can greatly affect the overall performance.
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Wu, ML., Chang, CH. (2014). Parallel Co-clustering with Augmented Matrices Algorithm with Map-Reduce. In: Bellatreche, L., Mohania, M.K. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2014. Lecture Notes in Computer Science, vol 8646. Springer, Cham. https://doi.org/10.1007/978-3-319-10160-6_17
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DOI: https://doi.org/10.1007/978-3-319-10160-6_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10159-0
Online ISBN: 978-3-319-10160-6
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