TY  - BOOK
AU  - Erica Joy P. Dequito and Krizsha Rei P. Enero.
TI  - A further enhancement of the closet algorithm applied in market basket analysis 
SN  - 2
AV  - QA76.75 D47 2016 
U1  - . 
PY  - 2016///
CY  - .
PB  - .
KW  - 2
KW  - 0
KW  - 6
KW  - 20
N1  - 5; F
N2  - ABSTRACT:  	Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and evaluate the probability of future events. Data mining is also known as Knowledge Discovery in Data. One of the best and well-known algorithms under data mining field is the Closet Algorithm developed by Jain Pei, Jiawei Han and Runying Mao. It is mainly remarkable for its efficiency for producing association rules.  	This study discusses Closet Algorithm and its shortcomings such as not having an optimal solution when the frequencies of distribution to all transactions that are equal, it doesn't take consideration for the confidence threshold for mining the most effective association rules and lastly, it doesn't consider the positioning of items in the application. With these problems, the researchers have come up with solutions. First they considered the quantity of items, then generated the minimum confidence threshold and lastly they generated the positioning  of items in the application. With these problems, the researchers have come up with solutions. First, they considered the quantity of items, then they generated the minimum confidence threshold and lastly they generated the positioning of items in the algorithm itself.  	After various tests and simulations, the algorithm is now enhanced and now has an optimal solution when the frequencies  of distribution to all transactions are equal. And the enhanced algorithm also has the capability to decide on the best possible location and promotion of items. Also, the existing algorithm does not have a confidence threshold. With the enhanced algorithm, it can now generate the confidence percentage for mining the most strong and effective association rules
ER  -