Datasets may contain outliers or bad observations that do not belong to the distribution to be analyzed. The cause may be an unreliable process of data collection, as in the case of using micro-array technologies for measurement of gene expression levels, or intentional actions, such as network intrusion. Outliers can lead to biased estimates and wrong conclusions about the object.
To process datasets with outliers, you can choose between the BACON outlier detection algorithm and robust methods considered in the following sections.
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