This document discusses techniques for discretizing numeric data values. It explains that discretization is the process of grouping numeric values into a limited set of intervals to reduce information loss. Supervised discretization uses class labels to determine cut points, while unsupervised techniques do not. Specific unsupervised methods covered include binning, histogram analysis, and cluster analysis. Binning evenly splits the data range into bins. Histogram analysis uses frequency distributions to find buckets. Cluster analysis partitions numeric values into groups using clustering algorithms. The document provides an overview of discretization and concept hierarchy generation for preprocessing numeric attributes in data.

1. K.B.Snega,M.sc(cs).,
NADAR SARASWATHI COLLEGE OF
ARTS AND SCIENCE, THENI.
Mining Frequent Patterns Association

2. DISCRETIZATION
 Discretization is the process of putting
values into buckets so that there are a
limited number of possible states.
 Data Discretization is defined as a
process of converting continuous data
attribute values into a finite set of
intervals with minimal loss of
information.

3. SUPERVISED DISCRETIZATION
 The Discretization of numeric's based
on the class variable is called supervised
Discretization.
 A widely used supervised Discretization
method is the “Fayyed-Irani” method
that uses information content to decide
where to cut up numeric ranges.

4. CONCEPT HIERARCHIES
 A concept hierarchy is explain a sequence
of mapping from a set of low-level concept
to high-level more general concept.
 Different type of concept hierarchies:
 Schema hierarchy
 Set grouping hierarchy
 Operation-derived hierarchy
 Rule-based hierarchy

5. DISCRETIZATION AND CONCEPT
HIERARCHY GENERATION FOR
NUMERIC DATA
 Binning
 Histogram analysis
 Clustering analysis
BINNING
 Binning is a top-down splitting technique based on
a specified number of bins.
 Binning is an unsupervised discretization
technique.

6. HISTOGRAM ANALYSIS
 Because histogram analysis does not use
class information so it is an
unsupervised discretization technique.
 Histograms partition the values of an
attribute into disjoint ranges called
buckets.

7. CLUSTER ANALYSIS
 Cluster analysis is a popular data
discretization method.
 A clustering algorithm can be applied to
discrete a numerical attribute of A by
partitioning the values of A into clusters or
groups.
 Each initial cluster or partition may be
further decomposed into several
subcultures, forming a lower level of the
hierarchy.

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