Research Article
An Efficient Outlier Detection Approach for Streaming Sensor Data Based on Neighbor Difference and Clustering
Algorithm 3
Outlier sources identification based on correlation (OSIC).
| | Input: the streaming sensors data after outlier detection | | | Output: outliers and their sources | | (01) | if there is no outlier in current window then | | (02) | exit | | (03) | else | | (04) | if the data instance is detected as point outlier then | | (05) | label its source as error | | (06) | end if | | (07) | if the data instance is detected as jump outlier then | | (08) | label its source as event | | (09) | end if | | (10) | if the data instance is detected as collective outlier or contextual outlier then | | (11) | calculate the correlation coefficient | | (12) | if correlation coefficient > th1then | | (13) | if more than half of attributes are labeled as outliers then | | (14) | label its source as event | | (15) | else | | (16) | label its source as error | | (17) | end if | | (18) | else if |correlation coefficient| > th2then | | (19) | read these correlative variables of data instances | | (20) | predict the mean and variance of normal values with 10-folder cross validation | | (21) | if the outlier is out of the predicted range then | | (22) | label its source as error | | (23) | else | | (24) | label its source as suspected event | | (25) | end if | | (26) | end if | | (27) | else | | (28) | label the source of collective outliers as unknown | | (29) | label the source of contextual outliers as normal | | (30) | end if | | (31) | end if |
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