2014 IEEE International Conference on Data Mining

14-17 Dec. 2014

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  • [Front cover]

    Publication Year: 2014, Page(s): C4
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  • [Title page i]

    Publication Year: 2014, Page(s): i
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  • [Title page iii]

    Publication Year: 2014, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2014, Page(s): iv
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  • Table of contents

    Publication Year: 2014, Page(s):v - xiv
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  • Message from the Conference Chairs

    Publication Year: 2014, Page(s):xv - xvi
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  • Message from the Program Co-Chairs

    Publication Year: 2014, Page(s):xvii - xviii
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  • Organizing Committee

    Publication Year: 2014, Page(s):xix - xx
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  • Program Committee

    Publication Year: 2014, Page(s):xxi - xxviii
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  • Keynotes

    Publication Year: 2014, Page(s):xxix - xxxii
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (129 KB) | HTML iconHTML

    These keynote discusses the following: Towards Mobile Visual Search; Ten Research Challenges in Data Science; and Understanding Global Change: Opportunities and Challenges for Data Driven Research. View full abstract»

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  • Tutorials

    Publication Year: 2014, Page(s):xxxiii - xxxvi
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (117 KB)

    These keynote discusses the following: Social Multimedia as Sensors; Node and Graph Similarity: Theory and Applications; Finding Repeated Structure in Time Series: Algorithms and Applications; and A Tutorial on Online Learning Methods for Big Data Analytics. View full abstract»

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  • Discriminative Learning on Exemplary Patterns of Sequential Numerical Data

    Publication Year: 2014, Page(s):1 - 10
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (492 KB) | HTML iconHTML

    One of the effective methodologies for time series classification is to identify informative subsequence patterns in time series and exploit them as discriminative features. Previous studies on this methodology have achieved promising results using a small number of individually selected patterns. However, there remain difficulties in finding a set of related patterns or patterns of a minor class,... View full abstract»

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  • Orthogonal Matching Pursuit for Sparse Quantile Regression

    Publication Year: 2014, Page(s):11 - 19
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (775 KB) | HTML iconHTML

    We consider new formulations and methods for sparse quantile regression in the high-dimensional setting. Quantile regression plays an important role in many data mining applications, including outlier-robust exploratory analysis in gene selection. In addition, the sparsity consideration in quantile regression enables the exploration of the entire conditional distribution of the response variable g... View full abstract»

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  • Quick Detection of High-Degree Entities in Large Directed Networks

    Publication Year: 2014, Page(s):20 - 29
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (261 KB) | HTML iconHTML

    In this paper we address the problem of quick detection of high-degree entities in large online social networks. Practical importance of this problem is attested by a large number of companies that continuously collect and update statistics about popular entities, usually using the degree of an entity as an approximation of its popularity. We suggest a simple, efficient, and easy to implement two-... View full abstract»

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  • Inferring Uncertain Trajectories from Partial Observations

    Publication Year: 2014, Page(s):30 - 39
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (535 KB) | HTML iconHTML

    The explosion in the availability of GPS-enabled devices has resulted in an abundance of trajectory data. In reality, however, majority of these trajectories are collected at a low sampling rate and only provide partial observations on their actually traversed routes. Consequently, they are mired with uncertainty. In this paper, we develop a technique called Infer Tra to infer uncertain trajectori... View full abstract»

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  • Tensor-Based Multi-view Feature Selection with Applications to Brain Diseases

    Publication Year: 2014, Page(s):40 - 49
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (347 KB) | HTML iconHTML

    In the era of big data, we can easily access information from multiple views which may be obtained from different sources or feature subsets. Generally, different views provide complementary information for learning tasks. Thus, multi-view learning can facilitate the learning process and is prevalent in a wide range of application domains. For example, in medical science, measurements from a serie... View full abstract»

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  • Collective Prediction of Multiple Types of Links in Heterogeneous Information Networks

    Publication Year: 2014, Page(s):50 - 59
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (385 KB) | HTML iconHTML

    Link prediction has become an important and active research topic in recent years, which is prevalent in many real-world applications. Current research on link prediction focuses on predicting one single type of links, such as friendship links in social networks, or predicting multiple types of links independently. However, many real-world networks involve more than one type of links, and differen... View full abstract»

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  • Factorized Similarity Learning in Networks

    Publication Year: 2014, Page(s):60 - 69
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (344 KB) | HTML iconHTML

    The problem of similarity learning is relevant to many data mining applications, such as recommender systems, classification, and retrieval. This problem is particularly challenging in the context of networks, which contain different aspects such as the topological structure, content, and user supervision. These different aspects need to be combined effectively, in order to create a holistic simil... View full abstract»

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  • Learning Local Semantic Distances with Limited Supervision

    Publication Year: 2014, Page(s):70 - 79
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1504 KB) | HTML iconHTML

    Recent advances in distance function learning have demonstrated that learning a good distance metric can greatly improve the performance in a wide variety of tasks in data mining and web search. A major problem in such scenarios is the limited labeled knowledge available for learning the user intentions. Furthermore, distances are inherently local, where a single global distance function may not c... View full abstract»

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  • Road Traffic Congestion Monitoring in Social Media with Hinge-Loss Markov Random Fields

    Publication Year: 2014, Page(s):80 - 89
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (531 KB) | HTML iconHTML

    Real-time road traffic congestion monitoring is an important and challenging problem. Most existing monitoring approaches require the deployment of infrastructure sensors or large-scale probe vehicles. Their installation is often expensive and temporal-spatial coverage is limited. Probe vehicle data are oftentimes noisy on urban arterials, and therefore insufficient to provide accurate congestion ... View full abstract»

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  • LorSLIM: Low Rank Sparse Linear Methods for Top-N Recommendations

    Publication Year: 2014, Page(s):90 - 99
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (314 KB) | HTML iconHTML

    In this paper, we notice that sparse and low-rank structures arise in the context of many collaborative filtering applications where the underlying graphs have block-diagonal adjacency matrices. Therefore, we propose a novel Sparse and Low-Rank Linear Method (Lor SLIM) to capture such structures and apply this model to improve the accuracy of the Top-N recommendation. Precisely, a sparse and low-r... View full abstract»

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  • Detecting Flow Anomalies in Distributed Systems

    Publication Year: 2014, Page(s):100 - 109
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (332 KB) | HTML iconHTML

    Deep within the networks of distributed systems, one often finds anomalies that affect their efficiency and performance. These anomalies are difficult to detect because the distributed systems may not have sufficient sensors to monitor the flow of traffic within the interconnected nodes of the networks. Without early detection and making corrections, these anomalies may aggravate over time and cou... View full abstract»

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  • Low-Rank Common Subspace for Multi-view Learning

    Publication Year: 2014, Page(s):110 - 119
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (441 KB) | HTML iconHTML

    Multi-view data is very popular in real-world applications, as different view-points and various types of sensors help to better represent data when fused across views or modalities. Samples from different views of the same class are less similar than those with the same view but different class. We consider a more general case that prior view information of testing data is inaccessible in multi-v... View full abstract»

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  • Sparse Real Estate Ranking with Online User Reviews and Offline Moving Behaviors

    Publication Year: 2014, Page(s):120 - 129
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2691 KB) | HTML iconHTML

    Ranking residential real estates based on investment values can provide decision making support for home buyers and thus plays an important role in estate marketplace. In this paper, we aim to develop methods for ranking estates based on investment values by mining users' opinions about estates from online user reviews and offline moving behaviors (e.g., Taxi traces, smart card transactions, check... View full abstract»

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  • Finding the Optimal Subspace for Clustering

    Publication Year: 2014, Page(s):130 - 139
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1827 KB) | HTML iconHTML

    The ability to simplify and categorize things is one of the most important elements of human thought, understanding, and learning. The corresponding explorative data analysis techniques -- dimensionality reduction and clustering -- have initially been studied by our community as two separate research topics. Later algorithms like CLIQUE, ORCLUS, 4C, etc. Performed clustering and dimensionality red... View full abstract»

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