An efficient outlier detection approach on weighted data stream based on minimal rare pattern mining

2019 ◽  
Vol 16 (10) ◽  
pp. 83-99 ◽  
Author(s):  
Saihua Cai ◽  
Ruizhi Sun ◽  
Shangbo Hao ◽  
Sicong Li ◽  
Gang Yuan
Keyword(s):  
Outlier Detection ◽  
Data Stream ◽  
Pattern Mining ◽  
Detection Approach ◽  
Weighted Data

scholarly journals WMFP-Outlier: An Efficient Maximal Frequent-Pattern-Based Outlier Detection Approach for Weighted Data Streams

2019 ◽  
Vol 48 (4) ◽  
pp. 505-521 ◽  
Author(s):  
Saihua Cai ◽  
Qian Li ◽  
Sicong Li ◽  
Gang Yuan ◽  
Ruizhi Sun
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Data Stream ◽  
Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Time Cost ◽  
Detection Approach ◽  
Maximal Frequent Pattern ◽  
Weighted Data

Since outliers are the major factors that affect accuracy in data science, many outlier detection approaches have been proposed for effectively identifying the implicit outliers from static datasets, thereby improving the reliability of the data. In recent years, data streams have been the main form of data, and the data elements in a data stream are not always of equal importance. However, the existing outlier detection approaches do not consider the weight conditions; hence, these methods are not suitable for processing weighted data streams. In addition, the traditional pattern-based outlier detection approaches incur a high time cost in the outlier detection phase. Aiming at overcoming these problems, this paper proposes a two-phase pattern-based outlier detection approach, namely, WMFP-Outlier, for effectively detecting the implicit outliers from a weighted data stream, in which the maximal frequent patterns are used instead of the frequent patterns to accelerate the process of outlier detection. In the process of maximal frequent-pattern mining, the anti-monotonicity property and MFP-array structure are used to accelerate the mining operation. In the process of outlier detection, three deviation indices are designed for measuring the degree of abnormality of each transaction, and the transactions with the highest degrees of abnormality are judged as outliers. Last, several experimental studies are conducted on a synthetic dataset to evaluate the performance of the proposed WMFP-Outlier approach. The results demonstrate that the accuracy of the WMFP-Outlier approach is higher compared to the existing pattern-based outlier detection approaches, and the time cost of the outlier detection phase of WMFP-Outlier is lower than those of the other four compared pattern-based outlier detection approaches.

Minimal weighted infrequent itemset mining-based outlier detection approach on uncertain data stream

2018 ◽  
Vol 32 (11) ◽  
pp. 6619-6639 ◽  
Author(s):  
Saihua Cai ◽  
Ruizhi Sun ◽  
Shangbo Hao ◽  
Sicong Li ◽  
Gang Yuan
Keyword(s):  
Outlier Detection ◽  
Data Stream ◽  
Uncertain Data ◽  
Itemset Mining ◽  
Detection Approach

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

2020 ◽  
Vol 191 ◽  
pp. 105268 ◽  
Author(s):  
Saihua Cai ◽  
Sicong Li ◽  
Gang Yuan ◽  
Shangbo Hao ◽  
Ruizhi Sun
Keyword(s):  
Outlier Detection ◽  
Data Stream ◽  
Uncertain Data ◽  
Detection Approach
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