Incremental Local Outlier Detection for Data Streams

By Dragoljub Pokrajac, Aleksandar Lazarevic, Longin Jan Latecki. IEEE Symposium on Computational Intelligence and Data Mining, 2007

451 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/3c5ad414aed20c19bf32dc64d0652db2f29b6c9c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Civil and Structural Engineering

Citations in the corpus

ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions (2022, background): “A natural way to characterize outliers is to take them to correspond to rare events that occur in low density parts of probability distribution ([25], [26]).”
Anomaly detection: A survey (2009, methodology): “2001; Tarassenko 1995], and video surveillance [Diehl and Hampshire 2002; Singh and Markou 2004; Pokrajac et al. 2007].”
Anomaly Detection in Time Series: A Comprehensive Evaluation (2022, methodology): “…[70] STAMP [156] MERLIN[97] DAD[154] BoehmerGraph[13] GrammarViz [120] TARZAN [71] NormA-SJ [15] NormA-smpl[15] KnorrSeq2[102] TSBitmap [144] ILOF[108] VALMOD [82] SurpriseEncoding[26] EnsembleGI [43] DADS[119] PST [128] SCRIMP++[163] LeftSTAMPi [156] Classic ML PCA[121] NetworkSVM[160]…”
Outlier Analysis (2013, methodology): “The work in [369] performs these updates in a judicious way, so as to efficiently determine the outliers.”
Anomaly Detection for Discrete Sequences: A Survey (2012, background): “Such domains often require the anomalies to be detected in such sequences in an online fashion, i.e., as soon as they occur [83], [84].”
MacroBase: Prioritizing Attention in Fast Data (2016, background): “A considerable subset of these techniques operates over data streams [6, 18, 65, 75].”
Progress in Outlier Detection Techniques: A Survey (2019, methodology): “The experimental results illustrated that their method achieved a better performance than some existing techniques [141], [164] for discovering significant outliers over the data stream.”
Loda: Lightweight on-line detector of anomalies (2016, methodology): “More efficient approaches based on bookkeeping (Pokrajac et al. 2007), better search structures like KDtrees (Bentley 1975), or approximate search (Andoni and Indyk 2006) have been adopted.”
Data preprocessing for anomaly based network intrusion detection: A review (2011, methodology): “The LOF algorithm was later extended to support incremental updates (Pokrajac et al., 2007).”
Anomaly detection in dynamic networks: a survey (2015, methodology): “Vertex detection [55, 56, 67, 106] Subgraph detection [3, 13, 30, 31] Change detection [38] Edge detection [22] Change detection [112] Vertex detection [129] Event detection [68, 72, 75, 77, 83, 84, 93, 113, 114, 115, 122] Change detection [7, 65, 66] Edge detection [1, 80] Subgraph detection [60, 8”