NDLI: Flexible and adaptive subspace search for outlier analysis

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Scholarly big data: information extraction and data mining

One size does not fit all: multi-granularity search of web forums

Graph-of-word and TW-IDF: new approach to ad hoc IR

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Building a large-scale corpus for evaluating event detection on twitter

Location prediction in social media based on tie strength

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Flexible and adaptive subspace search for outlier analysis

Content Provider	ACM Digital Library
Author	Böhm, Klemens Keller, Fabian Wixler, Andreas Müller, Emmanuel
Abstract	There exists a variety of traditional outlier models, which measure the deviation of outliers with respect to the full attribute space. However, these techniques fail to detect outliers that deviate only w.r.t. an attribute subset. To address this problem, recent techniques focus on a selection of subspaces that allow: (1) A clear distinction between clustered objects and outliers; (2) a description of outlier reasons by the selected subspaces. However, depending on the outlier model used, different objects in different subspaces have the highest deviation. It is an open research issue to make subspace selection adaptive to the outlier score of each object and flexible w.r.t. the use of different outlier models. In this work we propose such a flexible and adaptive subspace selection scheme. Our generic processing allows instantiations with different outlier models. We utilize the differences of outlier scores in random subspaces to perform a combinatorial refinement of relevant subspaces. Our refinement allows an individual selection of subspaces for each outlier, which is tailored to the underlying outlier model. In the experiments we show the flexibility of our subspace search w.r.t. various outlier models such as distance-based, angle-based, and local-density-based outlier detection.
Starting Page	1381
Ending Page	1390
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505560
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Outlier description Subspace search High dimen- sional data Outlier detection Data mining
Content Type	Text
Resource Type	Article

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