Towards Smart Data Mining Support Based on Annotation Beneficial Grouping Method

A data mining (DM) system requires many steps, such as pre-processing the data, execution of the data mining algorithm, and extracting mining results. They can be a straightforward but traditional procedure. Each step has multiple choices; only a few variations provide promising results. Both beginners and specialists in data mining require assistance with the vast reach and non-trivial experiences throughout the composition and collection of DM methods. The intelligent exploration assistant prototype enables customers who have access to a systemic list of valid DM processes to raise the ranks of these valid processes in accordance with a variety of criteria. This helps customer's select DM processes that are appropriate and may be more efficient than other options, and it also ensures that appropriate alternatives are not overlooked. The prototypes demonstrate that a single IDA is still capable of having relevant lists and performing successful classification even in basic classification procedures. The potential for an IDA to play a significant role as a medium for the exchange of information within a data mining community is investigated. In conclusion, it has been shown that the claims have been proved by demonstrating that cost-sensitive classes have used a more nuanced procedure utilizing information gained from the KDDCUP competition in 1998.