Clustering social audiences in business information networks. (April 2020)

• Record Type: Journal Article
• Authors: Zheng, Yu; Hu, Ruiqi; Fung, Sai-fu; Yu, Celina; Long, Guodong; Guo, Ting; Pan, Shirui
• Is Part Of: Pattern recognition. Volume 100(2020:Apr.)
• Publication Date: 2020-04
• Subjects: Machine learning -- Clustering -- Business information networks -- Social networks; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.107126
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose a factorization based clustering algorithm for business Information networks. The method performs co-clustering on features and customers simultaneously. The method provides a better understanding of functional roles of customers for companies. Results on 13 real enterprise datasets demonstrate the effectiveness of our algorithm. Abstract: Business information networks involve diverse users and rich content and have emerged as important platforms for enabling business intelligence and business decision making. A key step in an organizations business intelligence process is to cluster users with similar interests into social audiences and discover the roles they play within a business network. In this article, we propose a novel machine-learning approach, called CBIN, that co-clusters business information networks to discover and understand these audiences. The CBIN framework is based on co-factorization. The audience clusters are discovered from a combination of network structures and rich contextual information, such as node interactions and node-content correlations. Since what defines an audience cluster is data-driven, plus they often overlap, pre-determining the number of clusters is usually very difficult. Therefore, we have based CBIN on an overlapping clustering paradigm with a hold-out strategy to discover the optimal number of clusters given the underlying data. Experiments validate an outstanding performance by CBIN compared to other state-of-the-art algorithms on 13 real-world enterprise datasets.