Data warehousing and data mining processes rely heavily on the implementation of association rule mining. In order to bolster this claim, the study suggests a model that retrieves frequent itemsets from the database that are arranged in the form of star schema tabular database structures and has a fuzzy taxonomic structure at the backend. The study's goal was to create a new method from an existing one that uses fuzzy association rule-based mining in databases using ER models. The focus of the study is on the extraction of linguistic algorithm rules at multilevel structures in the form of tables from various databases in order to comprehend the functionality of these retrieved data item sets. The suggested data mining algorithm's operation is illustrated by an example in the conclusion. It may be used to quickly and easily generate multi-level fuzzy rules that are relevant to association mining techniques.
Praveen Arora,
Jagan Institute of Management Studies, Rohini Sector 5, Near Rithala Metro Station, New Delhi, India.
Sanjive Saxena,
Jagan Institute of Management Studies, Rohini Sector 5, Near Rithala Metro Station, New Delhi, India.
Silky Madan,
Jagan Institute of Management Studies, Rohini Sector 5, Near Rithala Metro Station, New Delhi, India.
Navneet Joshi,
Jagan Institute of Management Studies, Rohini Sector 5, Near Rithala Metro Station, New Delhi, India.
Please see the link here: https://stm.bookpi.org/NRAMCS-V5/article/view/7481
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