• Dr.R.Sharmila

Mining of intelligence rules from the database forms a vital application in the present business scenario as the mined patterns from the database can significantly be applied to have critical decisions in the over whole business process. Recently, a few of research have been presented in the literature about mining of interesting relation with the objective of improving the business. In this paper, an algorithm to mine the intelligence rules from the input data using the multi-objective optimization algorithm is proposed. Here, the objectives used are profit, quality and the proposed reliability. The objectives taken for rule mining, subsequently R-tree is modelled and is solved using the improved ABC algorithm. Hybridization is brought about in the initial population and also in the scout bee phase. The rules obtained from the ABC algorithm are useful for developing business strategies. The experimentation is carried using the datasets to evaluate the performance in terms of mined rules and the computation time. The analysis is carried out by varying the threshold and support. Performance metrics employed are number of optimized rules, computation time and memory usage. From the analysis, it can be seen that the number of optimised rules decrease with increase in threshold and support.

Intelligence Rules, Data Mining, Multi-Objective, Artificial Bee Colony algorithm, R-Tree, Profit, Quality, Genetic Algorithm

"MULTI-OBJECTIVE BASED IMPROVED ARTIFICIAL BEE COLONY ALGORITHM TO MINE THE BUSINESS INTELLIGENCE RULES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.511-524, June-2019, Available :http://www.jetir.org/papers/JETIR1906854.pdf

"MULTI-OBJECTIVE BASED IMPROVED ARTIFICIAL BEE COLONY ALGORITHM TO MINE THE BUSINESS INTELLIGENCE RULES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp511-524, June-2019, Available at : http://www.jetir.org/papers/JETIR1906854.pdf