| dc.description.abstract | mining is the area that helping extracting
the useful information by finding patterns or rules from the
existing dataset. By using the extracted information then
used to predict future tendencies and behavior patterns.
Association mining is a branch of data mining which used
to identify itemsets that take place frequently in a specific
dataset and to determine rules. Association mining can find
out the rules that predict the occurrence of an item with
regard to the similar occurrences of other in a particular
transaction. Eclat algorithm is kind of a frequent itemset
mining which is a sub section of the association mining
based on the mining frequent patterns by exploring the
vertical data format. Eclat algorithm was actually
developed for Market Basket Analysis which is an effective
technique in retail industry that helps the shop owner to
increase the sales distribution techniques. Market Basket
Analysis is completely done by the association rule mining
in which analyses the customer buying behavior against the
purchasing item from the shop. Eclat algorithm is the one
of the most effective ways to mining of large data set since
it follows the depth in search. When it comes to the real
world, the main objective of market basket analysis is to
gain maximized profit at all with the help of operational
research theories. In this approach, the condensed data is
used for mine the frequent itemset using the Eclat
algorithm. After all, one of the operational research
theories which are termed linear programming will use to
maximize the profits.
Support value and the Confidence value are the foremost
factors in generating the Eclat. Eclat algorithm abandons
Apriori’s breadth-first search for a recursive depth-first
search. Moreover, consideration of frequent items as well
as non-frequent items, considerably impact the profit
maximization. Because if the retail owner identified the
non-frequent itemset; can provide the promotions to the
customers. It will enhance the profit maximization.
Therefore, this research was mainly focused to identify
frequent itemset as well as the non-frequent itemset in a
market basket analysis alone with the profit maximization
using linear programming. This developed approach is
applied to a real world dataset and results were compared
considering Eclat algorithm and Eclat algorithm alone with
the linear programming separately. Finally, the results
conclude that proposed approach significantly increase the
profit. | |
