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A comprehensive survey of numeric and symbolic outlier mining techniques
Abstract
Data that appear to have different characteristics than the rest of the population are called outliers. Identifying outliers from huge data repositories is a very complex task called outlier mining. Outlier mining has been akin to finding needles in
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“Since the seminal work in [105], anomaly detection has been well studied and there exists a plethora of comprehensive surveys and reviews on it, including but not limited to References [1,5,25,36,37,134,135,161,165,231].”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [5]
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“According to Agyemang et al. [5], distribution-based anomaly detection techniques are intrinsically explainable.”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [5]
Citations
Cites
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
David, F. N., & Tukey, J. W. (1977) · Biometrics · doi:10.2307/2529486 · Cited by 12,707
Robust Regression and Outlier Detection
Piepel, G. F., Rousseeuw, P. J., & Leroy, A. M. (1989) · Technometrics · doi:10.2307/1268828 · Cited by 6,183
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551
BIRCH: an efficient data clustering method for very large databases
Zhang, T., Ramakrishnan, R., & Livny, M. (1996) · Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96 · doi:10.1145/233269.233324 · Cited by 3,935
A Survey of Outlier Detection Methodologies
Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596
Hawkins, D. M. (1980) · doi:10.1007/978-94-015-3994-4 · Cited by 2,982
BARNETT, V. D., Lewis, T., & Abeles, F. F. (1979) · Physics Today · doi:10.1063/1.2995737 · Cited by 2,834
Automatic subspace clustering of high dimensional data for data mining applications
Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276314 · Cited by 2,412
Efficient algorithms for mining outliers from large data sets
Ramaswamy, S., Rastogi, R. R., & Shim, K. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335437 · Cited by 1,908
Atkinson, A. C., BARNETT, V. D., & Lewis, T. (1995) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2983451 · Cited by 1,693
Algorithms for Mining Distance-Based Outliers in Large Datasets
Knorr, E. M., & Ng, R. T. (1998) · Cited by 1,550
Database mining: a performance perspective
Agrawal, R., Imieliński, T., & Swami, A. (1993) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.250074 · Cited by 1,514
Ben‐Hur, A., Horn, D., Siegelmann, H. T., & Vapnik, V. N. (2002) · doi:10.5555/944790.944807 · Cited by 1,370
STING: A Statistical Information Grid Approach to Spatial Data Mining
Wang, W. W., Yang, J., & Muntz, R. R. (1997) · Cited by 1,263
Distance-based outliers: algorithms and applications
Knorr, E. M., Ng, R. T., & Tucakov, V. (2000) · The VLDB Journal The International Journal on Very Large Data Bases · doi:10.1007/s007780050006 · Cited by 1,195
An efficient approach to clustering in large multimedia databases with noise
Hinneburg, A., & Keim, D. A. (1998) · KOPS (University of Konstanz) · Cited by 1,164
Outlier detection for high dimensional data
Aggarwal, C. C., & Yu, P. S. (2001) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/375663.375668 · Cited by 1,066
Fast Outlier Detection in High Dimensional Spaces
Angiulli, F., & Pizzuti, C. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45681-3_2 · Cited by 880
Knowledge discovery and data mining: towards a unifying framework
Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996) · Cited by 847
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
Sheikholeslami, G., Chatterjee, S., & Zhang, A. (1998) · Cited by 751
Outlier Detection Using Replicator Neural Networks
Hawkins, S., He, H., Williams, G., & Baxter, R. A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46145-0_17 · Cited by 750
What makes patterns interesting in knowledge discovery systems
Silberschatz, A., & Tuzhilin, A. (1996) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.553165 · Cited by 680
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Bay, S. D., & Schwabacher, M. (2003) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/956750.956758 · Cited by 675
The Grand Tour: A Tool for Viewing Multidimensional Data
Asimov, D. (1985) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0906011 · Cited by 660
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
Tang, J., Chen, Z., Fu, A. W.-C., & Cheung, D. W.-L. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47887-6_53 · Cited by 589
Question classification using support vector machines
Zhang, D., & Lee, W. S. (2003) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/860435.860443 · Cited by 585
Distributed data mining in credit card fraud detection
Chan, P. K., Fan, W., Prodromidis, A. L., & Stolfo, S. J. (1999) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.809570 · Cited by 540
Separate-and-Conquer Rule Learning
Fürnkranz, J. (1999) · Artificial Intelligence Review · doi:10.1023/a:1006524209794 · Cited by 531
Survey of fraud detection techniques
Kou, Y., Lu, C., Sirwongwattana, S., & Huang, Y.-P. (2004) · IEEE International Conference on Networking, Sensing and Control (ICNSC) · doi:10.1109/icnsc.2004.1297040 · Cited by 444
Pruning and summarizing the discovered associations
Liu, B., Hsu, W., & Ma, Y. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312216 · Cited by 421
Finding Intensional Knowledge of Distance-Based Outliers
Knorr, E. M., & Ng, R. T. (1999) · Cited by 419
Outlier mining in large high-dimensional data sets
Angiulli, F., & Pizzuti, C. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.31 · Cited by 368
A Linear Method for Deviation Detection in Large Databases
Arning, A., Agrawal, R., & Raghavan, P. (1996) · Cited by 363
Unsupervised Profiling Methods for Fraud Detection
Bolton, R. J., & Hand, D. J. (2002) · Cited by 360
On subjective measures of interestingness in knowledge discovery
Silberschatz, A., & Tuzhilin, A. (1995) · Cited by 356
An information theoretic approach to rule induction from databases
Smyth, P., & Goodman, R. M. (1992) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.149926 · Cited by 353
Mining top-n local outliers in large databases
Jin, W., Tung, A. K. H., & Han, J. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502554 · Cited by 334
A unifying framework for detecting outliers and change points from non-stationary time series data
Yamanishi, K., & Takeuchi, J. (2002) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775148 · Cited by 283
Outlier detection and motion segmentation
Torr, P., & Murray, D. (1993) · Other Conferences · doi:10.1117/12.150246 · Cited by 279
A unified notion of outliers: properties and computation
Knorr, E. M., & Ng, R. T. (1997) · Cited by 276
Computing depth contours of bivariate point clouds
Ruts, I., & Rousseeuw, P. J. (1996) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(96)00027-8 · Cited by 259
A belief-driven method for discovering unexpected patterns
Padmanabhan, B. (1998) · Cited by 242
Web classification using support vector machine
Sun, A., Lim, E., & Ng, W. K. (2002) · ACM International Workshop on Web Information and Data Management (WIDM) · doi:10.1145/584931.584952 · Cited by 222
Fuzzy support vector machines for pattern classification
Inoue, T., & Abe, S. (2002) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2001.939575 · Cited by 221
The interestingness of deviations
Piatetsky-Shapiro, G., & Matheus, C. J. (1994) · Cited by 213
An effective and efficient algorithm for high-dimensional outlier detection
Aggarwal, C. C., & Yu, P. S. (2004) · The VLDB Journal · doi:10.1007/s00778-004-0125-5 · Cited by 212
Novelty detection using extreme value statistics
Roberts, S. (1999) · IEE Proceedings - Vision Image and Signal Processing · doi:10.1049/ip-vis:19990428 · Cited by 190
Unexpectedness as a measure of interestingness in knowledge discovery
Padmanabhan, B., & Tuzhilin, A. (1999) · Decision Support Systems · doi:10.1016/s0167-9236(99)00053-6 · Cited by 190
OPTICS-OF: Identifying Local Outliers
Breunig, M. M., Kriegel, H., Ng, R. T., & Sander, J. (1999) · Lecture notes in computer science · doi:10.1007/978-3-540-48247-5_28 · Cited by 185
Detecting graph-based spatial outliers
Shekhar, S., Lu, C., & Zhang, P. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502567 · Cited by 168
Finding interesting patterns using user expectations
Liu, B., Hsu, W., Mun, L.-F., & Lee, H.-Y. (1999) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/69.824588 · Cited by 156
A new measure of overall potential influence in linear regression
Hadi, A. S. (1992) · Computational Statistics & Data Analysis · doi:10.1016/0167-9473(92)90078-t · Cited by 146
Padmanabhan, B., & Tuzhilin, A. (2000) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/347090.347103 · Cited by 145
The "DGX" distribution for mining massive, skewed data
Bi, Z., Faloutsos, C., & Korn, F. R. (2001) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502521 · Cited by 118
Exception Rule Mining with a Relative Interestingness Measure
Hussain, F., Liu, H., Suzuki, E., & Lü, H. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45571-x_11 · Cited by 114
Outlier Detection Algorithms in Data Mining Systems
Petrovskiy, M. (2003) · Programming and Computer Software · doi:10.1023/a:1024974810270 · Cited by 91
An outlier-based data association method for linking criminal incidents
Lin, S., & Brown, D. E. (2004) · Decision Support Systems · doi:10.1016/j.dss.2004.06.005 · Cited by 85
Discovering unexpected information from your competitors' web sites
Liu, B., Ma, Y., & Yu, P. S. (2001) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/502512.502534 · Cited by 84
Miller, R. C., & Myers, B. A. (2001) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/502348.502361 · Cited by 56
Scaling Up Inductive Learning with Massive Parallelism
Provost, F. J., & Aronis, J. M. (1996) · Machine Learning · doi:10.1007/bf00116898 · Cited by 56
Outlier Detection Integrating Semantic Knowledge
He, Z., Deng, S., & Xu, X. (2002) · Lecture notes in computer science · doi:10.1007/3-540-45703-8_12 · Cited by 49
Detecting region outliers in meteorological data
Zhao, J., Lu, C., & Kou, Y. (2003) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/956676.956683 · Cited by 48
Detecting Unusual and Deceptive Communication in Email
Keila, P. S., & Skillicorn, D. B. (2005) · QSpace (Queen's University Library) · Cited by 48
Parallel Mining of Outliers in Large Database
Hung, E., & Cheung, D. W.-L. (2002) · Distributed and Parallel Databases · doi:10.1023/a:1015608814486 · Cited by 45
ROR: rejection of outliers by rotations
Adam, A., Rivlin, E., & Shimshonl, I. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.899948 · Cited by 45
Fast discovery of unexpected patterns in data, relative to a Bayesian network
Jaroszewicz, S., & Scheffer, T. (2005) · doi:10.1145/1081870.1081887 · Cited by 44
Exceptional knowledge discovery in databases based on information theory
Suzuki, E., & Shimura, M. (1996) · Knowledge Discovery and Data Mining · Cited by 43
Extremal dynamics: A unifying physical explanation of fractals, 1/f noise, and activated processes
Miller, S. L., Miller, W. M., & McWhorter, P. J. (1993) · Journal of Applied Physics · doi:10.1063/1.353079 · Cited by 38
Mining web content outliers using structure oriented weighting techniques and N-grams
Agyemang, M., Barker, K. E., & Alhajj, R. S. (2005) · ACM Symposium on Applied Computing · doi:10.1145/1066677.1066788 · Cited by 31
Self-citationFramework for mining web content outliers
Agyemang, M., Barker, K. E., & Alhajj, R. (2004) · ACM Symposium on Applied Computing · doi:10.1145/967900.968022 · Cited by 29
Self-citationDetecting Graph-based Spatial Outliers: Algorithms and Applications
Shekhar, S., Lu, C., & Zhang, P. (2001) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 28
Beyond Keyword Filtering for Message and Conversation Detection
Skillicorn, D. B. (2005) · Lecture notes in computer science · doi:10.1007/11427995_19 · Cited by 18
Hybrid Approach to Web Content Outlier Mining Without Query Vector
Agyemang, M., Barker, K. E., & Alhajj, R. (2005) · Lecture notes in computer science · doi:10.1007/11546849_28 · Cited by 12
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Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.12.026 · Cited by 1,649
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.08.006 · Cited by 1,361
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Metadata
- Authors
- Agyemang, M., Barker, K., & Alhajj, R.
- Affiliations
- University of Calgary (CA)
- ORCID
- Barker, Ken
- Venue
- Intelligent Data Analysis
- Publisher
- SAGE Publications
- Published
- 2006-11-16
- Volume
- 10
- Issue
- 6
- Pages
- 521-538
- ISSN
- 1088-467X (ISSN-L)
- 1571-4128
- OpenAlex
- W2097627964
- Cited by
- 164
- References
- 76
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 6 · 2022: 21 · 2021: 7 · 2020: 9 · 2019: 17 · 2018: 15 · 2017: 15 · 2016: 8 · 2015: 11 · 2014: 7 · 2013: 13 · 2012: 13
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Intelligent Data Analysis (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- outlier detection, numerical data, symbolic data, data mining techniques
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