Developer Guide

Contents

Batch Processing

Algorithm Input

The multivariate BACON outlier detection algorithm accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.
Input ID
Input
data
Pointer to the
n
x
p
numeric table with the data for outlier detection. The input can be an object of any class derived from the
NumericTable
class.

Algorithm Parameters

The multivariate outlier detection algorithm has the following parameters, which depend on the computation method parameter
method
:
Parameter
Default Value
Description
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
or
double
.
initializationMethod
baconMedian
The initialization method, can be:
  • baconMedian
    - median-based method
  • defaultDense
    - Mahalanobis distance-based method
alpha
0.05
One-tailed probability that defines the (1 - α) quantile of the χ
2
distribution with
p
degrees of freedom.
Recommended value: α/
n
, where
n
is the number of observations.
toleranceToConverge
0.005
The stopping criterion. The algorithm is terminated if the size of the basic subset is changed by less than the threshold.

Algorithm Output

The multivariate BACON outlier detection algorithm calculates the result described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.
Result ID
Result
weights
Pointer to the
n
x 1 numeric table of zeros and ones. Zero in the
i
-th position indicates that the
i
-th feature vector is an outlier. By default, the result is an object of the
HomogenNumericTable
class, but you can define the result as an object of any class derived from
NumericTable
except the
PackedSymmetricMatrix
,
PackedTriangularMatrix,
and
CSRNumericTable
.

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804