de.lmu.ifi.dbs.elki.algorithm.outlier

Class AggarwalYuNaive<V extends NumberVector<?>>

java.lang.Object

de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm<OutlierResult>

de.lmu.ifi.dbs.elki.algorithm.outlier.AbstractAggarwalYuOutlier<V>

de.lmu.ifi.dbs.elki.algorithm.outlier.AggarwalYuNaive<V>

Type Parameters:

V - Vector type

All Implemented Interfaces:

Algorithm, OutlierAlgorithm, Parameterizable

@Title(value="BruteForce: Outlier detection for high dimensional data")
@Description(value="Examines all possible sets of k dimensional projections")
@Reference(authors="C.C. Aggarwal, P. S. Yu",
           title="Outlier detection for high dimensional data",
           booktitle="Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 2001), Santa Barbara, CA, 2001",
           url="http://dx.doi.org/10.1145/375663.375668")
public class AggarwalYuNaive<V extends NumberVector<?>>
extends AbstractAggarwalYuOutlier<V>

BruteForce provides a naive brute force algorithm in which all k-subsets of dimensions are examined and calculates the sparsity coefficient to find outliers. The evolutionary approach is implemented as AggarwalYuEvolutionary.

Reference:
Outlier detection for high dimensional data
C.C. Aggarwal, P. S. Yu
International Conference on Management of Data Proceedings of the 2001 ACM SIGMOD international conference on Management of data 2001, Santa Barbara, California, United States

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	AggarwalYuNaive.Parameterizer<V extends NumberVector<?>> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
private static Logging	LOG The logger for this class.

Fields inherited from class de.lmu.ifi.dbs.elki.algorithm.outlier.AbstractAggarwalYuOutlier

DONT_CARE, k, phi

Constructor Summary

Constructors

Constructor and Description
AggarwalYuNaive(int k, int phi) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
protected Logging	getLogger() Get the (STATIC) logger for this class.
OutlierResult	run(Relation<V> relation) Run the algorithm on the given relation.

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.outlier.AbstractAggarwalYuOutlier

buildRanges, computeSubspace, computeSubspaceForGene, getInputTypeRestriction, sparsity

Methods inherited from class de.lmu.ifi.dbs.elki.algorithm.AbstractAlgorithm

makeParameterDistanceFunction, run

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface de.lmu.ifi.dbs.elki.algorithm.outlier.OutlierAlgorithm

run

Field Detail

LOG

private static final Logging LOG

The logger for this class.

Constructor Detail

AggarwalYuNaive

public AggarwalYuNaive(int k,
               int phi)

Constructor.

Parameters:

k - K

phi - Phi

Method Detail

run

public OutlierResult run(Relation<V> relation)

Run the algorithm on the given relation.

Parameters:

relation - Relation

Returns:

Outlier detection result

getLogger

protected Logging getLogger()

Description copied from class: AbstractAlgorithm

Get the (STATIC) logger for this class.

Specified by:

getLogger in class AbstractAlgorithm<OutlierResult>

Returns:

the static logger