Implementation of Very Fast Decision Rules for Classification in Data Streams

In this work huge amount of continuous and rapid data records are considered to solve the stream mining problems such as extracting knowledge structures by using several algorithms. One of the most interpretable and flexible model for predictive data mining are decision rules. Few algorithms are proposed in our work to have a clear idea on the decision rules. One of it is the VFDR algorithm and its proposed versions. These versions are one pass and any time algorithms. It works online and produces ordered or unordered rule sets. An adaptive extension is enabled to detect changes and quickly adapt the decision model. In this extension we monitor the evolution performance metric to detect concept drift. This explicit change detection mechanism provides useful information about the dynamics of process generating data.