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2012 IEEE 12th International Conference on Data Mining

10-13 Dec. 2012

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Displaying Results 1 - 25 of 164
  • [Front cover]

    Publication Year: 2012, Page(s): C4
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  • [Title page i]

    Publication Year: 2012, Page(s): i
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  • [Title page iii]

    Publication Year: 2012, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2012, Page(s): iv
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  • Table of contents

    Publication Year: 2012, Page(s):v - xiv
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  • Message from General Co-chairs

    Publication Year: 2012, Page(s):xv - xvi
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  • Message from Program Co-chairs

    Publication Year: 2012, Page(s):xvii - xviii
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  • Conference Organization

    Publication Year: 2012, Page(s):xix - xxi
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  • Program Committee

    Publication Year: 2012, Page(s):xxii - xxvi
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  • Keynotes [3 abstracts]

    Publication Year: 2012, Page(s):xxvii - xxix
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (306 KB)

    Provides an abstract for each of the three keynote presentations and a brief professional biography of each presenter. View full abstract»

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  • Tutorial i: Outlier detection in high dimensional data

    Publication Year: 2012, Page(s):xxx - xxxii
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (108 KB)

    Summary form only given, as follows. High dimensional data in Euclidean space pose special challenges to data mining algorithms. These challenges are often indiscriminately subsumed under the term "curse of dimensionality", more concrete aspects being the so-called "distance concentration effect", the presence of irrelevant attributes concealing relevant information, or simply efficiency issues. I... View full abstract»

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  • Differentially Private Histogram Publishing through Lossy Compression

    Publication Year: 2012, Page(s):1 - 10
    Cited by:  Papers (11)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (617 KB) | HTML iconHTML

    Differential privacy has emerged as one of the most promising privacy models for private data release. It can be used to release different types of data, and, in particular, histograms, which provide useful summaries of a dataset. Several differentially private histogram releasing schemes have been proposed recently. However, most of them directly add noise to the histogram counts, resulting in un... View full abstract»

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  • Spotting Culprits in Epidemics: How Many and Which Ones?

    Publication Year: 2012, Page(s):11 - 20
    Cited by:  Papers (33)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (821 KB) | HTML iconHTML

    Given a snapshot of a large graph, in which an infection has been spreading for some time, can we identify those nodes from which the infection started to spread? In other words, can we reliably tell who the culprits are? In this paper we answer this question affirmatively, and give an efficient method called NETSLEUTH for the well-known Susceptible-Infected virus propagation model. Essentially, w... View full abstract»

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  • Self-Adjusting Models for Semi-supervised Learning in Partially Observed Settings

    Publication Year: 2012, Page(s):21 - 30
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (320 KB) | HTML iconHTML

    We present a new direction for semi-supervised learning where self-adjusting generative models replace fixed ones and unlabeled data can potentially improve learning even when labeled data is only partially-observed. We model each class data by a mixture model and use a hierarchical Dirichlet process (HDP) to model observed as well as unobserved classes. We extend the standard HDP model to accommo... View full abstract»

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  • Stream Classification with Recurring and Novel Class Detection Using Class-Based Ensemble

    Publication Year: 2012, Page(s):31 - 40
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (459 KB) | HTML iconHTML

    Concept-evolution has recently received a lot of attention in the context of mining data streams. Concept-evolution occurs when a new class evolves in the stream. Although many recent studies address this issue, most of them do not consider the scenario of recurring classes in the stream. A class is called recurring if it appears in the stream, disappears for a while, and then reappears again. Exi... View full abstract»

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  • Feature Weighting and Selection Using Hypothesis Margin of Boosting

    Publication Year: 2012, Page(s):41 - 50
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (311 KB) | HTML iconHTML

    Utilizing the concept of hypothesis margins to measure the quality of a set of features has been a growing line of research in the last decade. However, most previous algorithms have been developed under the large hypothesis margin principles of the 1-NN algorithm, such as Simba. Little attention has been paid so far to exploiting the hypothesis margins of boosting to evaluate features. Boosting i... View full abstract»

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  • GPU-Accelerated Feature Selection for Outlier Detection Using the Local Kernel Density Ratio

    Publication Year: 2012, Page(s):51 - 60
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (313 KB) | HTML iconHTML

    Effective outlier detection requires the data to be described by a set of features that captures the behavior of normal data while emphasizing those characteristics of outliers which make them different than normal data. In this work, we present a novel non-parametric evaluation criterion for filter-based feature selection which caters to outlier detection problems. The proposed method seeks the s... View full abstract»

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  • Sequential Alternating Proximal Method for Scalable Sparse Structural SVMs

    Publication Year: 2012, Page(s):61 - 70
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (350 KB) | HTML iconHTML

    Structural Support Vector Machines (SSVMs) have recently gained wide prominence in classifying structured and complex objects like parse-trees, image segments and Part-of-Speech (POS) tags. Typical learning algorithms used in training SSVMs result in model parameters which are vectors residing in a large-dimensional feature space. Such a high-dimensional model parameter vector contains many non-ze... View full abstract»

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  • Computational Television Advertising

    Publication Year: 2012, Page(s):71 - 80
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (362 KB) | HTML iconHTML

    Ever wonder why that Kia Ad ran during Iron Chef? Traditional advertising methodology on television is a fascinating mix of marketing, branding, measurement, and predictive modeling. While still a robust business, it is at risk with the recent growth of online and time-shifted (recorded) television. A particular issue is that traditional methods for television advertising are far less efficient th... View full abstract»

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  • Topic-Aware Social Influence Propagation Models

    Publication Year: 2012, Page(s):81 - 90
    Cited by:  Papers (26)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (394 KB) | HTML iconHTML

    We study social influence from a topic modeling perspective. We introduce novel topic-aware influence-driven propagation models that experimentally result to be more accurate in describing real-world cascades than the standard propagation models studied in the literature. In particular, we first propose simple topic-aware extensions of the well-known Independent Cascade and Linear Threshold models... View full abstract»

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  • GUISE: Uniform Sampling of Graphlets for Large Graph Analysis

    Publication Year: 2012, Page(s):91 - 100
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (418 KB) | HTML iconHTML

    Graphlet frequency distribution (GFD) has recently become popular for characterizing large networks. However, the computation of GFD for a network requires the exact count of embedded graphlets in that network, which is a computationally expensive task. As a result, it is practically infeasible to compute the GFD for even a moderately large network. In this paper, we propose GUISE, which uses a Ma... View full abstract»

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  • Hierarchical Multilabel Classification with Minimum Bayes Risk

    Publication Year: 2012, Page(s):101 - 110
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (499 KB) | HTML iconHTML

    Hierarchical multilabel classification (HMC) allows an instance to have multiple labels residing in a hierarchy. A popular loss function used in HMC is the H-loss, which penalizes only the first classification mistake along each prediction path. However, the H-loss metric can only be used on tree-structured label hierarchies, but not on DAG hierarchies. Moreover, it may lead to misleading predicti... View full abstract»

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  • The Mixture of Multi-kernel Relevance Vector Machines Model

    Publication Year: 2012, Page(s):111 - 120
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1286 KB) | HTML iconHTML

    We present a new regression mixture model where each mixture component is a multi-kernel version of the Relevance Vector Machine (RVM). In the proposed model, we exploit the enhanced modeling capability of RVMs due to their embedded sparsity enforcing properties. %The main contribution of this %work is the employment of RVM models as components of a mixture %model and their application to the time... View full abstract»

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  • Diffusion of Information in Social Networks: Is It All Local?

    Publication Year: 2012, Page(s):121 - 130
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (476 KB) | HTML iconHTML

    Recent studies on the diffusion of information in social networks have largely focused on models based on the influence of local friends. In this paper, we challenge the generalizability of this approach and revive theories introduced by social scientists in the context of diffusion of innovations to model user behavior. To this end, we study various diffusion models in two different online social... View full abstract»

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  • Efficient Pattern-Based Time Series Classification on GPU

    Publication Year: 2012, Page(s):131 - 140
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (510 KB) | HTML iconHTML

    Time series shapelet discovery algorithm finds subsequences from a set of time series for use as primitives for time series classification. This algorithm has drawn a lot of interest because of the interpretability of its results. However, computation requirements restrict the algorithm from dealing with large data sets and may limit its application in many domains. In this paper, we address this ... View full abstract»

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