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REMIX: Automated Exploration for Interactive Outlier Detection

Published: 13 August 2017 Publication History

Abstract

Outlier detection is the identification of points in a dataset that do not conform to the norm. Outlier detection is highly sensitive to the choice of the detection algorithm and the feature subspace used by the algorithm. Extracting domain-relevant insights from outliers needs systematic exploration of these choices since diverse outlier sets could lead to complementary insights. This challenge is especially acute in an interactive setting, where the choices must be explored in a time-constrained manner. In this work, we present REMIX, the first system to address the problem of outlier detection in an interactive setting. REMIX uses a novel mixed integer programming (MIP) formulation for automatically selecting and executing a diverse set of outlier detectors within a time limit. This formulation incorporates multiple aspects such as (i) an upper limit on the total execution time of detectors (ii) diversity in the space of algorithms and features, and (iii) meta-learning for evaluating the cost and utility of detectors. REMIX provides two distinct ways for the analyst to consume its results: (i) a partitioning of the detectors explored by REMIX into perspectives through low-rank non-negative matrix factorization; each perspective can be easily visualized as an intuitive heatmap of experiments versus outliers, and (ii) an ensembled set of outliers which combines outlier scores from all detectors. We demonstrate the benefits of REMIX through extensive empirical validation on real-world data.

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  • (2022)TAQIH, a tool for tabular data quality assessment and improvement in the context of health dataComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2018.12.029181:COnline publication date: 21-Apr-2022
  • (2021)FaultTracer: interactive visual exploration of fault propagation patterns in power grid simulation dataJournal of Visualization10.1007/s12650-020-00741-z24:5(1051-1064)Online publication date: 10-May-2021
  • (2020)Detecting Anomalies from Streaming Time Series using Matrix Profile and Shapelets Learning2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00066(376-383)Online publication date: Nov-2020
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cover image ACM Conferences
KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2017
2240 pages
ISBN:9781450348874
DOI:10.1145/3097983
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 August 2017

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Author Tags

  1. ensemble
  2. exploration
  3. interactive
  4. matrix factorization
  5. outlier detection
  6. visualization

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KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all
  • (2022)TAQIH, a tool for tabular data quality assessment and improvement in the context of health dataComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2018.12.029181:COnline publication date: 21-Apr-2022
  • (2021)FaultTracer: interactive visual exploration of fault propagation patterns in power grid simulation dataJournal of Visualization10.1007/s12650-020-00741-z24:5(1051-1064)Online publication date: 10-May-2021
  • (2020)Detecting Anomalies from Streaming Time Series using Matrix Profile and Shapelets Learning2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00066(376-383)Online publication date: Nov-2020
  • (2019)Oui! Outlier Interpretation on Multi‐dimensional Data via Visual AnalyticsComputer Graphics Forum10.1111/cgf.1368338:3(213-224)Online publication date: 10-Jul-2019
  • (2018)EnsembleLens: Ensemble-based Visual Exploration of Anomaly Detection Algorithms with Multidimensional DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2018.286482525:1(109-119)Online publication date: 7-Dec-2018

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