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2011 IEEE 11th International Conference on Data Mining

Date 11-14 Dec. 2011

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  • [Front cover]

    Publication Year: 2011, Page(s): C1
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  • [Title page i]

    Publication Year: 2011, Page(s): i
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  • [Title page iii]

    Publication Year: 2011, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2011, Page(s): iv
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  • Table of contents

    Publication Year: 2011, Page(s):v - xiv
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  • Message from the Conference General Chairs

    Publication Year: 2011, Page(s):xv - xvi
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  • Message from the Program Co-Chairs

    Publication Year: 2011, Page(s):xvii - xviii
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  • Organizing Committee

    Publication Year: 2011, Page(s):xix - xx
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  • Steering Committee

    Publication Year: 2011, Page(s): xxi
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  • Program Committee

    Publication Year: 2011, Page(s):xxii - xxix
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  • List of Reviewers

    Publication Year: 2011, Page(s):xxx - xxxiii
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  • Sponsors

    Publication Year: 2011, Page(s):xxxiv - xxxv
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  • Algorithms for Mining the Evolution of Conserved Relational States in Dynamic Networks

    Publication Year: 2011, Page(s):1 - 10
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2467 KB) | HTML iconHTML

    Dynamic networks have recently being recognized as a powerful abstraction to model and represent the temporal changes and dynamic aspects of the data underlying many complex systems. Significant insights regarding the stable relational patterns among the entities can be gained by analyzing temporal evolution of the complex entity relations. This can help identify the transitions from one conserved... View full abstract»

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  • Infrastructure Pattern Discovery in Configuration Management Databases via Large Sparse Graph Mining

    Publication Year: 2011, Page(s):11 - 20
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (471 KB) | HTML iconHTML

    A configuration management database (CMDB) can be considered to be a large graph representing the IT infrastructure entities and their inter-relationships. Mining such graphs is challenging because they are large, complex, and multi-attributed, and have many repeated labels. These characteristics pose challenges for graph mining algorithms, due to the increased cost of sub graph isomorphism (for s... View full abstract»

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  • Role-Behavior Analysis from Trajectory Data by Cross-Domain Learning

    Publication Year: 2011, Page(s):21 - 30
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1014 KB) | HTML iconHTML

    Behavior analysis using trajectory data presents a practical and interesting challenge for KDD. Conventional analyses address discriminative tasks of behaviors, e.g., classification and clustering typically using the subsequences extracted from the trajectory of an object as a numerical feature representation. In this paper, we explore further to identify the difference in the high-level semantics... View full abstract»

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  • Semi-supervised Feature Importance Evaluation with Ensemble Learning

    Publication Year: 2011, Page(s):31 - 40
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (234 KB) | HTML iconHTML

    We consider the problem of using a large amount of unlabeled data to improve the efficiency of feature selection in high dimensional datasets, when only a small set of labeled examples is available. We propose a new semi-supervised feature importance evaluation method (SSFI for short), that combines ideas from co-training and random forests with a new permutation-based out-of-bag feature importanc... View full abstract»

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  • COMET: A Recipe for Learning and Using Large Ensembles on Massive Data

    Publication Year: 2011, Page(s):41 - 50
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (259 KB) | HTML iconHTML

    COMET is a single-pass MapReduce algorithm for learning on large-scale data. It builds multiple random forest ensembles on distributed blocks of data and merges them into a mega-ensemble. This approach is appropriate when learning from massive-scale data that is too large to fit on a single machine. To get the best accuracy, IVoting should be used instead of bagging to generate the training subset... View full abstract»

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  • Overlapping Correlation Clustering

    Publication Year: 2011, Page(s):51 - 60
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4157 KB) | HTML iconHTML

    We introduce a new approach to the problem of overlapping clustering. The main idea is to formulate overlapping clustering as an optimization problem in which each data point is mapped to a small set of labels, representing membership to different clusters. The objective is to find a mapping so that the distances between data points agree as much as possible with distances taken over their label s... View full abstract»

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  • Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning

    Publication Year: 2011, Page(s):61 - 70
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1075 KB) | HTML iconHTML

    Transductive transfer learning is one special type of transfer learning problem, in which abundant labeled examples are available in the source domain and only unlabeled examples are available in the target domain. It easily finds applications in spam filtering, microblogging mining and so on. In this paper, we propose a general framework to solve the problem by mapping the input features in both ... View full abstract»

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  • Confidence in Predictions from Random Tree Ensembles

    Publication Year: 2011, Page(s):71 - 80
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (312 KB) | HTML iconHTML

    Obtaining an indication of confidence of predictions is desirable for many data mining applications. Such confidence levels, together with the predicted value, can inform on the certainty or extent of reliability that may be associated with the prediction. This can be useful, for example, where model outputs are used in making potentially costly decisions, and one may then focus on the higher conf... View full abstract»

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  • Mining Heavy Subgraphs in Time-Evolving Networks

    Publication Year: 2011, Page(s):81 - 90
    Cited by:  Papers (7)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (677 KB) | HTML iconHTML

    Networks from different genres are not static entities, but exhibit dynamic behavior. The congestion level of links in transportation networks varies in time depending on the traffic. Similarly, social and communication links are employed at varying rates as information cascades unfold. In recent years there has been an increase of interest in modeling and mining dynamic networks. However, limited... View full abstract»

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  • Multi-Class L2,1-Norm Support Vector Machine

    Publication Year: 2011, Page(s):91 - 100
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (291 KB) | HTML iconHTML

    Feature selection is an essential component of data mining. In many data analysis tasks where the number of data point is much less than the number of features, efficient feature selection approaches are desired to extract meaningful features and to eliminate redundant ones. In the previous study, many data mining techniques have been applied to tackle the above challenging problem. In this paper,... View full abstract»

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  • SolarMap: Multifaceted Visual Analytics for Topic Exploration

    Publication Year: 2011, Page(s):101 - 110
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1214 KB) | HTML iconHTML

    Documents in rich text corpora often contain multiple facets of information. For example, an article from a medical document collection might consist of multifaceted information about symptoms, treatments, causes, diagnoses, prognoses, and preventions. Thus, documents in the collection may have different relations across each of these various facets. Topic analysis and exploration for such multi-r... View full abstract»

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  • Efficiently Mining Unordered Trees

    Publication Year: 2011, Page(s):111 - 120
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (499 KB) | HTML iconHTML

    Frequent tree patterns have many applications in different domains such as XML document mining, user web log analysis, network routing and bioinformatics. In this paper, we first introduce three new tree encodings and accordingly present an efficient algorithm for finding frequent patterns from rooted unordered trees with the assumption that children of every node in database trees are identically... View full abstract»

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  • CEMiner -- An Efficient Algorithm for Mining Closed Patterns from Time Interval-Based Data

    Publication Year: 2011, Page(s):121 - 130
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (410 KB) | HTML iconHTML

    The mining of closed sequential patterns has attracted researchers for its capability of using compact results to preserve the same expressive power as conventional mining. However, existing studies only focus on time point-based data. Few research efforts have elaborated on discovering closed sequential patterns from time interval-based data, where each data persists for a period of time. Mining ... View full abstract»

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