• Cluster-weighted modeling
• Curse of dimensionality
• Determining the number of clusters in a data set
• Parallel coordinates
• Structured data analysis
4.6 References
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[9] Microsoft academic search: most cited data mining ar- ticles: DBSCAN is on rank 24, when accessed on: 4/18/2010
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[11] Ankerst, Mihael; Breunig, Markus M.; Kriegel, Hans- Peter; Sander, Jörg (1999). “OPTICS: Ordering Points To Identify the Clustering Structure”. ACM SIGMOD inter- national conference on Management of data.ACM Press. pp. 49–60.CiteSeerX:10 .1 .1 .129 .6542.
[12] Achtert, E.; Böhm, C.; Kröger, P. (2006). “DeLi- Clu: Boosting Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking”. LNCS: Advances in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science 3918: 119–128. doi:10.1007/11731139_16. ISBN 978-3-540- 33206-0.
[13] Roy, S.; Bhattacharyya, D. K. (2005). “An Approach to find Embedded Clusters Using Density Based Tech- niques”. LNCS Vol.3816.Springer Verlag. pp. 523–535. [14] Sculley, D. (2010). Web-scale k-means clustering. Proc.
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[15] Huang, Z. (1998). “Extensions to the k-means algo- rithm for clustering large data sets with categorical val- ues”. Data Mining and Knowledge Discovery 2: 283–304. [16] R. Ng and J. Han. “Efficient and effective clustering method for spatial data mining”. In: Proceedings of the 20th VLDB Conference, pages 144-155, Santiago, Chile, 1994.
[17] Tian Zhang, Raghu Ramakrishnan, Miron Livny. “An Efficient Data Clustering Method for Very Large Databases.” In: Proc. Int'l Conf. on Management of Data, ACM SIGMOD, pp. 103–114.
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(2005). “Automatic Subspace Clustering of High Dimen- sional Data”. Data Mining and Knowledge Discovery 11: 5.doi:10.1007/s10618-005-1396-1.
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4.7 External links
Chapter 5
Anomaly detection
In data mining, anomaly detection (or outlier detec- tion) is the identification of items, events or observations which do not conform to an expected pattern or other items in adataset.[1] Typically the anomalous items will translate to some kind of problem such asbank fraud, a structural defect, medical problems or finding errors in text. Anomalies are also referred to asoutliers, novelties, noise, deviations and exceptions.[2]In particular in the context of abuse and network intru- sion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection meth- ods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, acluster analysisalgorithm may be able to detect the mi- cro clusters formed by these patterns.[3]
Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques de- tect anomalies in an unlabeled test data set under the as- sumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been la- beled as “normal” and “abnormal” and involves training a classifier (the key difference to many otherstatistical clas- sificationproblems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behav- ior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.
5.1 Applications
Anomaly detection is applicable in a variety of domains, such asintrusion detection,fraud detection, fault detec- tion, system health monitoring, event detection in sensor networks, and detecting Eco-system disturbances. It is often used in preprocessing to remove anomalous data from the dataset. Insupervised learning, removing the anomalous data from the dataset often results in a statis-
tically significant increase in accuracy.[4][5]
5.2 Popular techniques
Several anomaly detection techniques have been pro- posed in literature. Some of the popular techniques are:
• Density-based techniques (k-nearest neigh- bor,[6][7][8] local outlier factor,[9] and many more variations of this concept[10]).
• Subspace-[11] and correlation-based[12] outlier de- tection for high-dimensional data.[13]
• One classsupport vector machines.[14] • Replicatorneural networks.
• Cluster analysisbased outlier detection.[15]
• Deviations fromassociation rulesand frequent item- sets.
• Fuzzy logic based outlier detection.
• Ensemble techniques, usingfeature bagging,[16][17] score normalization[18][19] and different sources of diversity.[20][21]