Research Article
An Efficient Outlier Detection Approach for Streaming Sensor Data Based on Neighbor Difference and Clustering
Algorithm 1
Outlier detection based on neighbor difference (ODND).
| | Input: streaming sensor data, threshold | | | Output: outliers and their types | | (01) | calculate the curDiff | | (02) | if isAbnormal = true then | | (03) | count = count + 1 | | (04) | end if | | (05) | if curDiff outside threshold then | | (06) | if isAbnormal = true the | | (07) | if curAbnormal and abDiff has opposite sign then | | (08) | if count = 1 then | | (09) | current data are labeled as true point outlier | | (10) | else | | (11) | if count in threshold then | | (12) | all data between current data and the data corresponding to abDiff are labeled as candidate collective outliers | | (13) | else if count outside threshold then | | (14) | the data corresponding to abDiff is labeled as a candidate jump outlier | | (15) | end if | | (16) | end if | | (17) | reset the temporary variables | | (18) | end if | | (19) | else | | (20) | if count in threshold then | | (21) | label the data corresponding to abDiff as a true jump outlier | | (22) | reset the temporary variables | | (23) | end if | | (24) | end if | | (25) | end if |
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