| description |
This chapter presents the core of the DWFIST approach, which is
concerned with supporting the analysis and exploration of frequent
itemsets and derived patterns, e.g. association rules, in
transactional datasets. The goal of this new approach is to provide
(1) flexible pattern-retrieval capabilities without requiring the
original data during the analysis phase, and (2) a standard modeling
for data warehouses of frequent itemsets allowing an easier
development and reuse of tools for analysis and exploration of
itemset-based patterns. Instead of storing the original datasets,
our approach organizes frequent itemsets holding on different
partitions of the original transactions in a data warehouse that
retains sufficient information for future analysis. A running
example for mining calendar-based patterns on data streams is
presented. Staging area tasks are discussed and standard conceptual
and logical schemas are presented. Properties of this standard
modeling allow to retrieve frequent itemsets holding on any set of
partitions along with upper and lower bounds on their frequency
counts. Furthermore, precision guarantees for some interestingness
measures of association rules are provided as well.
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