user manual

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Chapter 4
preconditions. Apriori requires that input and output fields all be categorical but
delivers better performance because it is opti mized for this type of data.
The CA RMA model extracts a set of rule s from the data w i t hout requiring you to
specify input or target fields. In contrast to Apriori the CARMA node offers build
settings for rule support (support for both antecedent and consequent) rathe r than just
antecedent support. This means that the rules generated can be use d for a wider variety
of ap plications—for example, to find a list of products or services (antecede nts)
whose consequent is the item that you want to promote this holiday season.
The Sequence node discovers association rules in sequential or time-oriented data. A
sequence is a list of item sets that tends to occur in a predictable order. For example, a
customer who purchases a razor and aftershave lotion may purchase shaving cream
the next time he shops. The Sequence node is based on the CARMA association rules
algorithm, which uses an efficient two-pass method for finding sequences.
Segmentation Models
Segmentation models divide the data into segments, or clusters, of records that have similar
patterns of input fields. As they are only interes ted in the input fields, segmentation models have
no concept of output or target fields. Examples of segmentation models are Kohonen networks,
K-Mea ns clustering, two-step clustering and anomaly detection.
Segmentation models (also known as “clustering models”) a r e useful in ca ses where the specific
result is unknown (for example, when identifying new patterns of fraud, or when identifying
groups of interest in your custom er bas e). Clusterin g models focus on identifying groups of
similar records and labeling the records according to the group to which they belong. This is
done without the benefit of p r ior k nowledge about the gr oups and their characteristics, and it
distingu is hes clustering models fr om the other m odeling techniques in that there is no predefined
output or target field for the model to predict. There are no right or wrong answers for these
models . Their value is determined by thei r ability to capture inte r esting groupings in the data an d
provide usef ul descriptions of those gr oupings. Clustering models are ofte n used to create clusters
or segments that are then used as inputs in subsequen t analyses (f or exa mple, by segmenting
potential customers into homogeneous subgroups).