Fast Algorithms for Outlier Detection | Zendy

Fawaz A. Masoud | Zendy; Mohâ€TMd Belal Al- Zoubi | Zendy; Imad Salah | Zendy; Ali Al-Dahoud | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Fast Algorithms for Outlier Detection

Author(s) -

Fawaz A. Masoud,

Mohâ€TMd Belal Al- Zoubi,

Imad Salah,

Ali Al-Dahoud

Publication year - 2008

Publication title -

journal of computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.161

H-Index - 28

eISSN - 1552-6607

pISSN - 1549-3636

DOI - 10.3844/jcssp.2008.129.132

Subject(s) - computer science , anomaly detection , artificial intelligence , algorithm , data mining , pattern recognition (psychology)

Finding fast algorithms to detect outliers (as unusual objects) by their distance to neighboring objects is a big desire. Two algorithms were proposed to detect outliers quickly. The first was based on the Partial Distance (PD) algorithm and the second was an improved version of the PD algorithm. It was found that the proposed algorithms reduced the number of distance calculations compared to the nested-loop method

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research