Data Mining Techniques for Segmentation - Data Mining Techniques in CRM: Inside Customer Segmentation - page 65

CHAPTER THREE

Data Mining Techniques
for Segmentation

SEGMENTING CUSTOMERS WITH DATA MINING
TECHNIQUES

In this chapter we focus on the data mining modeling techniques used for
segmentation. We will present in detail some of the most popular and efficient
clustering algorithms, their settings, strengths, and capabilities, and we will see
them in action through a simple example that aims at preparing readers for the
real-world applications to be presented in subsequent chapters.
Although clustering algorithms can be directly applied to input data, a
recommended preprocessing step is the application of a data reduction technique
that can simplify and enhance the segmentation process by removing redundant
information. This approach, although optional, is highly recommended, as it adjusts
for possible input data intercorrelations, ensuring rich and unbiased segmentation
solutions that equally account for all the underlying data dimensions. Therefore,
this chapter also presents in detail principal components analysis (PCA), an
established data reduction technique typically used for grouping the original fields
into meaningful components.

PRINCIPAL COMPONENTS ANALYSIS

PCA is a statistical technique used to reduce the data of the original input fields.
It derives a limited number of compound measures that can efficiently substitute
for the original inputs while retaining most of their information.