Building Blocks for Discovering Common Patterns and Class Association Rules in Incremental Data

Abstract

The term "data mining" refers to the job of obtaining meaningful, appealing and unusual
patterns from vast quantities of data. Association Rule Mining and Classification are
considered to be important tasks of data analysis in data mining. Associative Classification
(AC), which is a combination of association rule mining and classification, has emerged as an
efficient classification model, and offers higher accuracy than the traditional classification
methods. The algorithms of frequent pattern mining and classification assume that the
databases are static, and hence, the batch-processing method is used. However, the real-time
databases are usually record-based and they are incremental in nature, where the set of records
is being added to the database. In this case, existing batch-processing methods do not use the
previously mined information in incrementally growing databases. This motivates the
requirement for incremental techniques that maintain and update the mining results as the
database expands. Maintenance of frequent patterns and class association rules is crucial,
particularly in the context of frequent pattern mining and classification. Hence, this research
work is focused on providing effective mining methods for frequent patterns and associationbased
classification, when the database is added incrementally. It proposes a framework for
frequent pattern mining and class association rule mining from the incremental datasets. The
framework is divided into three stages: (1) mining of frequent patterns from an incremental
data stream; (2) mining of Class Association Rules (CARs) from incremental datasets and
generation of Weighted Class Association Rules (WCARs); and (3) mining of Constraint Class
Association Rules (CCARs) and building of classifier by applying the rule pruning and
selection techniques. The proposed incremental algorithm is tested to compare the execution
time of the incremental and non-incremental CCAR algorithm with different selectivity values.
The execution time of the proposed Incremental CCAR algorithm gives a speedup of 0.93ms
to 1.11ms compared to CCAR algorithm.