Mining Association Rules from Incremental Data set

ISBN-13: 9786204980942 / Angielski / Miękka / 68 str.

Satyavathi Nedendla; Balakrishna Eppakayala; Rama Abbidi

Opis

Association Rule Mining (ARM) in data mining provides quality association rules based on measures such as support and confidence. These rules are interpreted by domain experts for making well-informed decisions. However, there is an issue with ARM when the dataset is subjected to changes from time to time. Discovering rules by reinventing wheel, scanning entire dataset every time in other words, consumes more memory, processing power and time. This is still an open problem due to proliferation of different data structures being used for extracting frequent item sets. An algorithm is proposed for update of mined association rules when dataset changes occur. The proposed algorithm outperforms the traditional approach as it mines association rules incrementally and dynamically updates mined association rules.

O autorze

Dr.N.Satyavathi working as a Head of the Department ,CSE in an Prestigious Institution. Having 17 Years of Experience in Teaching. Published various research papers in various Scopus journals and International Conferences. Continuing research in the area of datamining.