2015 IEEE International Conference on Data Mining

14-17 Nov. 2015

Filter Results

Displaying Results 1 - 25 of 158
  • [Front cover]

    Publication Year: 2015, Page(s): C4
    Request permission for commercial reuse | PDF file iconPDF (1888 KB)
    Freely Available from IEEE
  • [Title page i]

    Publication Year: 2015, Page(s): i
    Request permission for commercial reuse | PDF file iconPDF (97 KB)
    Freely Available from IEEE
  • [Title page iii]

    Publication Year: 2015, Page(s): iii
    Request permission for commercial reuse | PDF file iconPDF (136 KB)
    Freely Available from IEEE
  • [Copyright notice]

    Publication Year: 2015, Page(s): iv
    Request permission for commercial reuse | PDF file iconPDF (118 KB)
    Freely Available from IEEE
  • Table of contents

    Publication Year: 2015, Page(s):v - xiv
    Request permission for commercial reuse | PDF file iconPDF (170 KB)
    Freely Available from IEEE
  • Message from the Conference Chairs

    Publication Year: 2015, Page(s):xv - xvi
    Request permission for commercial reuse | PDF file iconPDF (81 KB) | HTML iconHTML
    Freely Available from IEEE
  • Message from the Program Co-Chairs

    Publication Year: 2015, Page(s):xvii - xviii
    Request permission for commercial reuse | PDF file iconPDF (82 KB) | HTML iconHTML
    Freely Available from IEEE
  • Organization

    Publication Year: 2015, Page(s):xix - xxi
    Request permission for commercial reuse | PDF file iconPDF (111 KB)
    Freely Available from IEEE
  • Program Committee

    Publication Year: 2015, Page(s):xxii - xxxiii
    Request permission for commercial reuse | PDF file iconPDF (140 KB)
    Freely Available from IEEE
  • External Reviewers

    Publication Year: 2015, Page(s):xxxiv - xxxvii
    Request permission for commercial reuse | PDF file iconPDF (94 KB)
    Freely Available from IEEE
  • Efficient Graphlet Counting for Large Networks

    Publication Year: 2015, Page(s):1 - 10
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (558 KB) | HTML iconHTML

    From social science to biology, numerous applications often rely on graphlets for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level. While graphlets have witnessed a tremendous success and impact in a variety of domains, there has yet to be a fast and efficient approach for computing the frequencies of these subgraph patterns. How... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Diamond Sampling for Approximate Maximum All-Pairs Dot-Product (MAD) Search

    Publication Year: 2015, Page(s):11 - 20
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (619 KB) | HTML iconHTML

    Given two sets of vectors, A = {a1→, . . . , am→} and B = {b1→, . . . , bn→}, our problem is to find the top-t dot products, i.e., the largest |ai→ · bj→| among all possible pairs. This is a fundamental mathematical problem that appears in numerous data applications involving similarity search, link prediction, and collaborative filtering. We propos... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Information Source Detection via Maximum A Posteriori Estimation

    Publication Year: 2015, Page(s):21 - 30
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (722 KB) | HTML iconHTML

    The problem of information source detection, whose goal is to identify the source of a piece of information from a diffusion process (e.g., computer virus, rumor, epidemic, and so on), has attracted ever-increasing attention from research community in recent years. Although various methods have been proposed, such as those based on centrality, spectral and belief propagation, the existing solution... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Influential Sustainability on Social Networks

    Publication Year: 2015, Page(s):31 - 40
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (297 KB) | HTML iconHTML

    In this paper, we study a novel paradigm of viral marketing with the goal to sustain the influential effectiveness in the network. We study from real cases such as the Ice Bucket Challenges for the ALS awareness, and figure out the "easy come and easy go" phenomenon in the marketing promotion. Such a natural property is fully unexplored in the literature, but it will violate the need of many marke... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Towards Frequent Subgraph Mining on Single Large Uncertain Graphs

    Publication Year: 2015, Page(s):41 - 50
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2204 KB) | HTML iconHTML

    Uncertainty is intrinsic to a wide spectrum of real-life applications, which inevitably applies to graph data. Representative uncertain graphs are seen in bio-informatics, social networks, etc. This paper motivates the problem of frequent subgraph mining on single uncertain graphs. We present an enumeration-evaluation algorithm to solve the problem. By showing support computation on an uncertain g... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Ensemble of Diverse Sparsifications for Link Prediction in Large-Scale Networks

    Publication Year: 2015, Page(s):51 - 60
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (985 KB) | HTML iconHTML

    Previous research has aimed to lower the cost of handling large networks by reducing the network size via sparsification. However, when many edges are removed from the network, the information that can be used for link prediction becomes rather limited, and the prediction accuracy thereby drops significantly. To address this issue, we propose a framework called Diverse Ensemble of Drastic Sparsifi... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Modeling Emerging, Evolving and Fading Topics Using Dynamic Soft Orthogonal NMF with Sparse Representation

    Publication Year: 2015, Page(s):61 - 70
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (767 KB) | HTML iconHTML

    Dynamic topic models (DTM) are of great use to analyze the evolution of unobserved topics of a text collection over time. Recent years have witnessed the explosive growth of streaming text data emerging from online media, which creates an unprecedented need for DTMs for timely event analysis. While there have been some matrix factorization methods in the literature for dynamic topic modeling, furt... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Unobtrusive Sensing Incremental Social Contexts Using Fuzzy Class Incremental Learning

    Publication Year: 2015, Page(s):71 - 80
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1783 KB) | HTML iconHTML

    By utilizing captured characteristics of surrounding contexts through widely used Bluetooth sensor, user-centric social contexts can be effectively sensed and discovered by dynamic Bluetooth information. At present, state-of-the-art approaches for building classifiers can basically recognize limited classes trained in the learning phase; however, due to the complex diversity of social contextual b... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Learning Predictive Substructures with Regularization for Network Data

    Publication Year: 2015, Page(s):81 - 90
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1757 KB) | HTML iconHTML

    Learning a succinct set of substructures that predicts global network properties plays a key role in understanding complex network data. Existing approaches address this problem by sampling the exponential space of all possible subnetworks to find ones of high prediction accuracy. In this paper, we develop a novel framework that avoids sampling by formulating the problem of predictive subnetwork l... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Jackknifing Documents and Additive Smoothing for Naive Bayes with Scarce Data

    Publication Year: 2015, Page(s):91 - 100
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (360 KB) | HTML iconHTML

    Naïve Bayes (NB) classifiers are well-suited to several applications owing to their easy interpretability and maintainability. However, text classification is often hampered by the lack of adequate training data. This motivates the question: how can we train NB more effectively whentraining data is very scarce?In this paper, we introduce an established subsampling techniquefrom statistics ... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Network Clustering via Maximizing Modularity: Approximation Algorithms and Theoretical Limits

    Publication Year: 2015, Page(s):101 - 110
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (273 KB) | HTML iconHTML

    Many social networks and complex systems are found to be naturally divided into clusters of densely connected nodes, known as community structure (CS). Finding CS is one of fundamental yet challenging topics in network science. One of the most popular classes of methods for this problem is to maximize Newman's modularity. However, there is a little understood on how well we can approximate the max... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Exceptionally Monotone Models -- The Rank Correlation Model Class for Exceptional Model Mining

    Publication Year: 2015, Page(s):111 - 120
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (715 KB) | HTML iconHTML

    Exceptional Model Mining strives to find coherent subgroups of the dataset where multiple target attributes interact in an unusual way. One instance of such an investigated form of interaction is Pearson's correlation coefficient between two targets. EMM then finds subgroups with an exceptionally linear relation between the targets. In this paper, we enrich the EMM toolbox by developing the more g... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Knowing an Object by the Company it Keeps: A Domain-Agnostic Scheme for Similarity Discovery

    Publication Year: 2015, Page(s):121 - 130
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (267 KB) | HTML iconHTML

    Appropriately defining and then efficiently calculating similarities from large data sets are often essential in data mining, both for building tractable representations and for gaining understanding of data and generating processes. Here we rely on the premise that given a set of objects and their correlations, each object is characterized by its context, i.e. its correlations to the other object... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Accurate Estimation of Generalization Performance for Active Learning

    Publication Year: 2015, Page(s):131 - 140
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (361 KB) | HTML iconHTML

    Active learning is a crucial method in settings where a human labeling of instances is challenging to obtain. The typical active learning loop builds a model from a few labeled instances, chooses informative unlabeled instances, asks an Oracle (i.e. a human) to label them and then rebuilds the model. Active learning is widely used with much research attention focused on determining which instances... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Discovery of College Students in Financial Hardship

    Publication Year: 2015, Page(s):141 - 150
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (502 KB) | HTML iconHTML

    College students with financial difficulties refer to those whose families can hardly afford their high tuition in universities, and should be supported by modern funding system. Indeed, students' economic plight negatively impact their mental health, academic performance, as well as their personal and social life. While funding students in financial hardship is widely accepted, there is limited u... View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.