Local outlier factor

Known as: LOF 
In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander… (More)
Wikipedia

Topic mentions per year

Topic mentions per year

1975-2017
010203019752017

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2015
2015
Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of… (More)
  • figure 1
  • figure 3
  • figure 5
  • figure 6
  • figure 7
Is this relevant?
2012
2012
In this paper we introduce the unsupervised machine-learning algorithm named Local Outlier Factor (LOF), for health risk… (More)
  • figure 1
  • figure 2
Is this relevant?
2011
2011
An effective algorithm for removing impulse noise from corrupted images is presented under the framework of switching median… (More)
  • figure 1
  • table I
  • table II
  • table III
  • figure 3
Is this relevant?
2010
2010
The Local Outlier Factor (LOF) is a very powerful anomaly detection method available in machine learning and classification. The… (More)
Is this relevant?
Highly Cited
2009
Highly Cited
2009
Detecting outliers which are grossly different from or inconsistent with the remaining dataset is a major challenge in real-world… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • table 1
Is this relevant?
2009
2009
  • Zengan Gao
  • 2009 International Conference on Management and…
  • 2009
Financial institutions’ capability in recognizing suspicious money laundering transactional behavioral patterns (SMLTBPs) is… (More)
  • figure 1
Is this relevant?
Highly Cited
2008
Highly Cited
2008
Detecting outliers in a large set of data objects is a major data mining task aiming at finding different mechanisms responsible… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2007
Highly Cited
2007
Outlier detection has recently become an important problem in many industrial and financial applications. This problem is further… (More)
  • figure 2
  • figure 1
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2001
Highly Cited
2001
Outlier detection is an important task in data mining with numerous applications, including credit card fraud detection, video… (More)
Is this relevant?
Highly Cited
2000
Highly Cited
2000
For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?