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- W2382174816 abstract "In order to solve the problem of outlier detection for categorical data streams,an outlier detection model for data streams based on attribute associations and match difference degree was proposed,called as AAMDD.This model builds an association rule library off-line and updates it with the incremental method.Meanwhile,it maintains the data streams by using time-sensitive sliding windows(TimeSW).In a time step,the AAMDD matches data in current window with association rules in the association rule library and calculates the match difference degree(MDD).Then,outliers can be identified on-line through different MDDs.An algorithm for the AAMDD was given,called as AAMDD-algorithm.The experiment results show that compared with the FODFP-Stream algorithm,the AAMDD-algorithm has on average 5.51%and 37.43%improvements respectively in detection precision and efficiency,and its recall is above 77%.It can be used to detect outliers in transaction data streams." @default.
- W2382174816 created "2016-06-24" @default.
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- W2382174816 date "2013-01-01" @default.
- W2382174816 modified "2023-09-23" @default.
- W2382174816 title "Outlier Detection Model for Data Streams Based on Attribute Associations and Match Difference Degree" @default.
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