Statistical Analysis and Data Mining: The ASA Data Science Journal

Cover image for Vol. 8 Issue 3

Edited By: David Madigan

Online ISSN: 1932-1872

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  • To catch a fake: Curbing deceptive Yelp ratings and venues

    To catch a fake: Curbing deceptive Yelp ratings and venues

    System overview of Marco. Marco relies on social, temporal and spatial signals gleaned from Yelp, to extract novel features. The features are used by the venue classifier module to label venues (deceptive vs. legitimate) based on the collected data. Section 5 describes Marco in detail.

  • To catch a fake: Curbing deceptive Yelp ratings and venues

    To catch a fake: Curbing deceptive Yelp ratings and venues

    Timelines of positive reviews of three deceptive venues (see Section 4.2). Each venue has several significant spikes in its number of daily positive reviews.

  • A predictive framework for modeling healthcare data with evolving clinical interventions

    A predictive framework for modeling healthcare data with evolving clinical interventions

    Trend over major medical interventions performed between 2007 and 2011 for the patients hospitalized with acute myocardial infarction (AMI). Usage of intervention normalized by the number of all interventions in a year. Interventions decrease, increase, or remain unchanged.

  • A predictive framework for modeling healthcare data with evolving clinical interventions

    A predictive framework for modeling healthcare data with evolving clinical interventions

    Results with synthetic dataset-II. (a) Mixture proportions of topics over years: true (first row) and inferred (second row) (b) Usage of mixture distributions over years, inferred using dHDP-LR (c) Usages of mixture distributions over years, inferred using HDP-LR.

  • A predictive framework for modeling healthcare data with evolving clinical interventions

    A predictive framework for modeling healthcare data with evolving clinical interventions

    Mixture distributions of intervention topics over time for AMI cohort: Patients are grouped over 3 months (a,b) or 1 year (c). A group can use either an existing distribution with little change, or a new one. Diagonals show the amount of change introduced and off-diagonals show the usage of a particular distribution (column) by other data groups (row), both in terms of number of patients (color-coded). (a) dHDP–data grouped in 3-month intervals. Legend: Q1:1-3 months, Q2: 4-6 months, Q3: 7-9 months, and Q4: 10-12 months. Two unique distributions are found–distributions in or after 2008(Q3) differ completely from the past. Prior to 2008(Q3) (Policy I) or after (Policy II), only small changes occured. (b) dHDP–data grouped in years. At lower granularity, dHDP fails to discover any change in distribution.

  • Summarizing and understanding large graphs

    Summarizing and understanding large graphs

    Illustration of the graph decomposition and the generation of the candidate structures. (a) Initial toy graph, (b) Slashburn on the toy graph and (c) candidate structures (in circles)

  • Summarizing and understanding large graphs

    Summarizing and understanding large graphs

    Enron: Adjacency matrix of the top near-bipartite core found by VoG, corresponding to email communication about an “affair,” as well as for a smaller near-bipartite core found by VoG representing email activity regarding a skiing trip

  • To catch a fake: Curbing deceptive Yelp ratings and venues
  • To catch a fake: Curbing deceptive Yelp ratings and venues
  • A predictive framework for modeling healthcare data with evolving clinical interventions
  • A predictive framework for modeling healthcare data with evolving clinical interventions
  • A predictive framework for modeling healthcare data with evolving clinical interventions
  • Summarizing and understanding large graphs
  • Summarizing and understanding large graphs

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