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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Parallel Co-clustering with Augmented Matrices Algorithm with Map-Reduce
verfasst von : Meng-Lun Wu, Chia-Hui Chang
Erschienen in: Data Warehousing and Knowledge Discovery
Verlag: Springer International Publishing
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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.