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2013 IEEE 13th International Conference on Data Mining

7-10 Dec. 2013

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Displaying Results 1 - 25 of 173
  • [Front cover]

    Publication Year: 2013, Page(s): C4
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  • [Title page i]

    Publication Year: 2013, Page(s): i
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  • [Title page iii]

    Publication Year: 2013, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2013, Page(s): iv
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  • Table of contents

    Publication Year: 2013, Page(s):v - xv
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  • Message from the Conference Chairs

    Publication Year: 2013, Page(s):xvi - xvii
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  • Message from the Program Co-Chairs

    Publication Year: 2013, Page(s):xviii - xix
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  • Conference Organizers

    Publication Year: 2013, Page(s):xx - xxii
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  • Program Committee

    Publication Year: 2013, Page(s):xxiii - xxx
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  • Keynotes

    Publication Year: 2013, Page(s):xxxi - xxxvi
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (199 KB)

    Provides an abstract for each of the keynote presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Tutorials [3 abstracts]

    Publication Year: 2013, Page(s):xxxvii - xxxix
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (81 KB)

    Provides an abstract of the three tutorial presentations. The complete presentation was not made available for publication as part of the conference proceedings. View full abstract»

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  • List of sponsors

    Publication Year: 2013, Page(s): xl
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  • Tree-Like Structure in Large Social and Information Networks

    Publication Year: 2013, Page(s):1 - 10
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1268 KB) | HTML iconHTML

    Although large social and information networks are often thought of as having hierarchical or tree-like structure, this assumption is rarely tested. We have performed a detailed empirical analysis of the tree-like properties of realistic informatics graphs using two very different notions of tree-likeness: Gromov's d-hyperbolicity, which is a notion from geometric group theory that measures how tr... View full abstract»

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  • Subgraph Enumeration in Dynamic Graphs

    Publication Year: 2013, Page(s):11 - 20
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (360 KB) | HTML iconHTML

    A fundamental problem in many applications involving social and biological networks is to identify and count the number of embeddings of a given small sub graph in a large graph. Often, they involve dynamic graphs, in which the graph changes incrementally (e.g., by edge addition/deletion). We study the Dynamic Sub graph Enumeration (DSE) Problem, where the goal is to maintain a dynamic data struct... View full abstract»

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  • A Masking Index for Quantifying Hidden Glitches

    Publication Year: 2013, Page(s):21 - 30
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (348 KB) | HTML iconHTML

    Data glitches are errors in a data set, they are complex entities that often span multiple attributes and records. When they co-occur in data, the presence of one type of glitch can hinder the detection of another type of glitch. This phenomenon is called masking. In this paper, we define two important types of masking, and we propose a novel, statistically rigorous indicator called masking index ... View full abstract»

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  • CSI: Charged System Influence Model for Human Behavior Prediction

    Publication Year: 2013, Page(s):31 - 40
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (334 KB) | HTML iconHTML

    Social influence has been widely studied in areas of viral marketing, information diffusion and health care. Currently, most influence models only deal with a single influence without the interference of other influences. Also, the influence spreading in previous models must be triggered by individuals who have been activated by the influence. In this paper, we argue that it is the attraction from... View full abstract»

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  • Context-Aware MIML Instance Annotation

    Publication Year: 2013, Page(s):41 - 50
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (255 KB) | HTML iconHTML

    In multi-instance multi-label (MIML) instance annotation, the goal is to learn an instance classifier while training on a MIML dataset, which consists of bags of instances paired with label sets, instance labels are not provided in the training data. The MIML formulation can be applied in many domains. For example, in an image domain, bags are images, instances are feature vectors representing seg... View full abstract»

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  • Maximizing Expected Model Change for Active Learning in Regression

    Publication Year: 2013, Page(s):51 - 60
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (259 KB) | HTML iconHTML

    Active learning is well-motivated in many supervised learning tasks where unlabeled data may be abundant but labeled examples are expensive to obtain. The goal of active learning is to maximize the performance of a learning model using as few labeled training data as possible, thereby minimizing the cost of data annotation. So far, there is still very limited work on active learning for regression... View full abstract»

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  • The Pairwise Gaussian Random Field for High-Dimensional Data Imputation

    Publication Year: 2013, Page(s):61 - 70
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (337 KB) | HTML iconHTML

    In this paper, we consider the problem of imputation (recovering missing values) in very high-dimensional data with an arbitrary covariance structure. The modern solution to this problem is the Gaussian Markov random field (GMRF). The problem with applying a GMRF to very high-dimensional data imputation is that while the GMRF model itself can be useful even for data having tens of thousands of dim... View full abstract»

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  • Controlling Attribute Effect in Linear Regression

    Publication Year: 2013, Page(s):71 - 80
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (285 KB) | HTML iconHTML

    In data mining we often have to learn from biased data, because, for instance, data comes from different batches or there was a gender or racial bias in the collection of social data. In some applications it may be necessary to explicitly control this bias in the models we learn from the data. This paper is the first to study learning linear regression models under constraints that control the bia... View full abstract»

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  • Active Matrix Completion

    Publication Year: 2013, Page(s):81 - 90
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1039 KB) | HTML iconHTML

    Recovering a matrix from a sampling of its entries is a problem of rapidly growing interest and has been studied under the name of matrix completion. It occurs in many areas of engineering and applied science. In most machine learning and data mining applications, it is possible to leverage the expertise of human oracles to improve the performance of the system. It is therefore natural to extend t... View full abstract»

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  • Modeling Temporal Adoptions Using Dynamic Matrix Factorization

    Publication Year: 2013, Page(s):91 - 100
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (409 KB) | HTML iconHTML

    The problem of recommending items to users is relevant to many applications and the problem has often been solved using methods developed from Collaborative Filtering (CF). Collaborative Filtering model-based methods such as Matrix Factorization have been shown to produce good results for static rating-type data, but have not been applied to time-stamped item adoption data. In this paper, we adopt... View full abstract»

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  • Modeling Preferences with Availability Constraints

    Publication Year: 2013, Page(s):101 - 110
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (272 KB) | HTML iconHTML

    User preferences are commonly learned from historical data whereby users express preferences for items, e.g., through consumption of products or services. Most work assumes that a user is not constrained in their selection of items. This assumption does not take into account the availability constraint, whereby users could only access some items, but not others. For example, in subscription-based ... View full abstract»

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  • wRACOG: A Gibbs Sampling-Based Oversampling Technique

    Publication Year: 2013, Page(s):111 - 120
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (455 KB) | HTML iconHTML

    As machine learning techniques mature and are used to tackle complex scientific problems, challenges arise such as the imbalanced class distribution problem, where one of the target class labels is under-represented in comparison with other classes. Existing over sampling approaches for addressing this problem typically do not consider the probability distribution of the minority class while synth... View full abstract»

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  • Efficient Visualization of Large-Scale Data Tables through Reordering and Entropy Minimization

    Publication Year: 2013, Page(s):121 - 130
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1730 KB) | HTML iconHTML

    Visualization of data tables with n examples and m columns using heat maps provides a holistic view of the original data. As there are n! ways to order rows and m! ways to order columns, and data tables are typically ordered without regard to visual inspection, heat maps of the original data tables often appear as noisy images. However, if rows and columns of a data table are ordered such that sim... View full abstract»

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