• Corpus ID: 8109230

Peer Group Analysis - Local Anomaly Detection in Longitudinal Data

@inproceedings{Bolton2001PeerGA,
  title={Peer Group Analysis - Local Anomaly Detection in Longitudinal Data},
  author={Richard J. Bolton and David J. Hand},
  year={2001}
}
Peer group analysis is a new tool for monitoring behavior over time in data mining situations. In particular, the tool detects individual objects that begin to behave in a way distinct from objects to which they had previously been similar. Each object is selected as a target object and is compared with all other objects in the database, using either external comparison criteria or internal criteria summarizing earlier behavior patterns of each object. Based on this comparison, a peer group of… 
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References

SHOWING 1-10 OF 13 REFERENCES

Temporal sequence learning and data reduction for anomaly detection

An approach that transforms temporal sequences of discrete, unordered observations into a metric space via a similarity measure that encodes intra-attribute dependencies and demonstrates that it can accurately differentiate the profiled user from alternative users when the available features encode sufficient information.

Data Mining for Fun and Profit

Data qual- ity is a particularly troublesome issue in data mining applications, and this is examined.

Activity monitoring: noticing interesting changes in behavior

It is shown that two superficially different tasks, news story monitoring and intrusion detection, can be expressed naturally within the framework, and show that key differences in solution methods can be compared.

Combining Data Mining and Machine Learning for Effective Fraud Detection

This paper combines data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior, and uses a rulelearning program to uncover indicators of fraudulent behavior from a large database of cellular calls.

Intrusion Detection via System Call Traces

Preliminary work in analyzing system call traces, particularly their structure during normal and anomalous behavior, reveals that normal program behavior can be described compactly using deterministic finite automata.

A sense of self for Unix processes

A method for anomaly detection is introduced in which "normal" is defined by short-range correlations in a process' system calls. Initial experiments suggest that the definition is stable during…

Giga-Mining

The motivation for massive tracking is discussed and the definition and the computation of one of the more interesting bytes in the profile are described and fully described.

Methodological issues in data mining

The nature of the new science of data mining is examined, drawing attention to the concepts and ideas it has inherited from areas such as statistics, machine learning, and database technology.…

A decomposition of multi-dimensional point-sets with applications to k-nearest-neighbors and n-body potential fields (preliminary version)

The notion of a well-separated pair decomposition of points in d-dimensional space is defined and the resulting decomposition is applied to the efficient computation of nearest neighbors and body potential fields.

A decomposition of multidimensional point sets with applications to k-nearest-neighbors and n-body potential fields

The notion of a well-separated pair decomposition of points in d-dimensional space is defined and the resulting decomposition is applied to the efficient computation of nearest neighbors and body potential fields.