Co-clustering with augmented matrix

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Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/106476

Title:	Co-clustering with augmented matrix
Authors:	張嘉惠;Wu, Meng-Lun;Chang, Chia-Hui;Liu, Rui-Zhe
Contributors:	資訊電機學院資訊工程學系
Keywords:	Advertisements;Algorithms;Applied sciences;Artificial Intelligence;Classification;Clustering;Cobalt;Collaboration;Computer Science;Computer science;control theory;systems;Computer systems and distributed systems. User interface;Data analysis;Data mining;Data processing. List processing. Character string processing;Descriptions;Dyadics;Exact sciences and technology;Information theory;Machines;Manufacturing;Mechanical Engineering;Memory organisation. Data processing;On-line systems;Online advertising;Preprocessing;Processes;Random variables;Recommender systems;Software
Date:	2013-07-01
Issue Date:	2026-04-23 13:24:23 (UTC+8)
Publisher:	Springer Netherlands;Boston: Springer US
Abstract:	摘要： Clustering plays an important role in data mining as many applications use it as a preprocessing step for data analysis. Traditional clustering focuses on the grouping of similar objects, while two-way co-clustering can group dyadic data (objects as well as their attributes) simultaneously. Most co-clustering research focuses on single correlation data, but there might be other possible descriptions of dyadic data that could improve co-clustering performance. In this research, we extend ITCC (Information Theoretic Co-Clustering) to the problem of co-clustering with augmented matrix. We proposed CCAM (Co-Clustering with Augmented Matrix) to include this augmented data for better co-clustering. We apply CCAM in the analysis of on-line advertising, where both ads and users must be clustered. The key data that connect ads and users are the user-ad link matrix, which identifies the ads that each user has linked; both ads and users also have their feature data, i.e. the augmented matrix. To evaluate the proposed method, we use two measures: classification accuracy and K - L divergence. The experiment is done using the advertisements and user data from Morgenstern, a financial social website that focuses on the advertisement agency. The experiment results show that CCAM provides better performance than ITCC since it considers the use of augmented matrix during clustering. 其他題名： Appl Intell 出版者： Boston: Springer US 出版日期： 2013-07-01 出處： Applied intelligence (Dordrecht, Netherlands), 2013-07, Vol.39 (1), p.153-164 資源來源： ABI/INFORM Global (ProQuest Business Suite) (LAB) 版權： Springer Science+Business Media New York 2012 版權： 2015 INIST-CNRS 版權： Springer Science+Business Media New York 2013 識別號： ISSN: 0924-669X 識別號： EISSN: 1573-7497 識別號： DOI: 10.1007/s10489-012-0401-9
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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