Parallel co-clustering with augmented matrices algorithm with map-reduce

Meng Lun Wu, Chia Hui Chang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
主出版物標題Data Warehousing and Knowledge Discovery - 16th International Conference, DaWaK 2014, Proceedings
發行者Springer Verlag
頁面183-194
頁數12
ISBN(列印)9783319101590
DOIs
出版狀態已出版 - 2014
事件16th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2014 - Munich, Germany
持續時間: 2 9月 20144 9月 2014

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
8646 LNCS
ISSN(列印)0302-9743
ISSN(電子)1611-3349

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???event.eventtypes.event.conference???16th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2014
國家/地區Germany
城市Munich
期間2/09/144/09/14

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