Study about Rule Mining for Multiple Tables with Fuzzy Data | Chapter 05 | Novel Research Aspects in Mathematical and Computer Science Vol. 1

 The study looks on mining association rules in databases with a large number of tables and fuzzy data, as well as their taxonomy. Many data mining algorithms have been created to cope with databases comprised of a single table with fuzzy taxonomic structures built on top of it. This study makes use of fuzzy data from many tables that were constructed using ER models. Each entity table keeps track of all attributes associated with a specific item, while the relationship table keeps track of relationships between different entities. The study's main purpose is to handle a large number of tables at different levels. The goal of the research is to combine the previously published methods Extended Apriori and Apriori star to build a new algorithm. The study will aid in the identification of relevant outcomes from ambiguous data in database tables.

Author(S) Details

Praveen Arora
Jagan Institute of Management Studies, Rohini, Delhi, India.

Priyanka Gandhi
Jagan Institute of Management Studies, Rohini, Delhi, India.

Geeta Sharma
Jagan Institute of Management Studies, Rohini, Delhi, India.

Sanjive Saxena
Jagan Institute of Management Studies, Rohini, Delhi, India.

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