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MiFI-Outlier: Minimal infrequent itemset-based outlier detection approach on uncertain data stream

  • Published:2020-03 Issue: Volume:191 Page:105268
  • ISSN:0950-7051
  • Container-title:Knowledge-Based Systems
  • language:en
  • Short-container-title:Knowledge-Based Systems

Author:

Cai Saihua,Li Sicong,Yuan Gang,Hao Shangbo,Sun Ruizhi

Funder

Chinese Universities Scientific Fund

Fundamental Research Funds for the Central Universities, China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference42 articles.

1. Finding efficiencies in frequent pattern mining from big uncertain data;Leung;World Wide Web,2017

2. A functional data analysis approach to traffic volume forecasting;Wagner-Muns;IEEE Trans. Intell. Transp. Syst.,2018

3. Meteorological data analysis using mapreduce;Fang;Sci. World J.,2014

4. Mining community and inferring friendship in mobile social networks;Xu;Neurocomputing,2016

5. A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives;Lee;Future Gener. Comput. Syst.,2017

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1. An Anomaly Detection Approach Based on Bidirectional Temporal Convolutional Network and Multi-Head Attention Mechanism;Information Technology and Control;2024-03-22

2. Outlier and Trend Detection Using Approximate Median and Median Absolute Deviation;2022 5th International Conference on Computational Intelligence and Networks (CINE);2022-12-01

3. Deep Belief Neural Network (DBNN)-Based Categorization of Uncertain Data Streams;International Journal of Software Innovation;2022-10-25

4. Detecting Outliers in Data Streams Based on Minimum Rare Pattern Mining and Pattern Matching;Information Technology and Control;2022-06-23

5. A survey of outlier detection in high dimensional data streams;Computer Science Review;2022-05
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