Co-clustering documents and words using bipartite spectral graph partitioning
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Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fully automatic cross-associations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
XML Filtering Using Dynamic Hierarchical Clustering of User Profiles
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Measuring serendipity: connecting people, locations and interests in a mobile 3G network
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Lower bounds for the partitioning of graphs
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Cellular data network infrastructure characterization and implication on mobile content placement
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
AccuLoc: practical localization of performance measurements in 3G networks
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Cellular data network infrastructure characterization and implication on mobile content placement
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Identifying diverse usage behaviors of smartphone apps
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Celleration: loss-resilient traffic redundancy elimination for cellular data
Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications
Characterization of 3G control-plane signaling overhead from a data-plane perspective
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Mosaic: quantifying privacy leakage in mobile networks
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
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With widespread popularity of smart phones, more and more users are accessing the Internet on the go. Understanding mobile user browsing behavior is of great significance for several reasons. For example, it can help cellular (data) service providers (CSPs) to improve service performance, thus increasing user satisfaction. It can also provide valuable insights about how to enhance mobile user experience by providing dynamic content personalization and recommendation, or location-aware services. In this paper, we try to understand mobile user browsing behavior by investigating whether there exists distinct "behavior patterns" among mobile users. Our study is based on real mobile network data collected from a large 3G CSP in North America. We formulate this user behavior profiling problem as a "co-clustering" problem, i.e., we group both users (who share similar browsing behavior), and browsing profiles (of like-minded users) simultaneously. We propose and develop a scalable co-clustering methodology, Phantom, using a novel hourglass model. The proposed hourglass model first reduces the dimensions of the input data and performs divisive hierarchical co-clustering on the lower dimensional data; it then carries out an expansion step that restores the original dimensions. Applying Phantom to the mobile network data, we find that there exists a number of prevalent and distinct behavior patterns that persist over time, suggesting that user browsing behavior in 3G cellular networks can be captured using a small number of co-clusters. For instance, behavior of most users can be classified as either homogeneous (users with very limited set of browsing interests) or heterogeneous (users with very diverse browsing interests), and such behavior profiles do not change significantly at either short (30-min) or long (6 hour) time scales.