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AR-MBA - Coggle Diagram
AR-MBA
AR Implementation
For marketing, increasing sales can be achieved by reorganizing items so that items sold together are always found together
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Google Reader site which is a web application where users can subscribe to feeds from news sites or blogs
Association Rule & MBA
Association Rule is a data mining technique to find associative rules between a combination of items or a job to determine which attributes will be obtained together
Market Basket Analysis uses what information is bought by consumers to provide a sign or information that is who they are and why they made the purchase
Market Basket Analysis provides an understanding of merchandise by telling us which products are possible to buy simultaneously and which products are more approved for promotion
Parameters of AR-MBA
Support is the frequency of A or how frequently an item appears in the dataset. It is defined as the fraction of the transaction T that contains the itemset X.
Confidence indicates how often the rule has been found to be true. Or how often the items X and Y occur together in the dataset when the occurrence of X is already given. It is the ratio of the transaction that contains X and Y to the number of records that contain X.
Lift ratio is a value that shows the validity of the transaction process and provides information on whether the item was purchased together with other items.
Algorithm of AR-MBA
Apriori Algorithm
A priori algorithm is an algorithm to reduce the search space for combination items so that analysis can be done more quickly
Main Processes
Join (merging).
In this process, each item is combined with other items until no more combinations are formed
Prune (pruning)
In this process, the results of the combined items are then trimmed using the minimum support specified by the user
FP-Growth Algorithm
The FP-Growth algorithm is a development of the Apriori algorithm so that the shortcomings of the Apriori algorithm are corrected by the FP-Growth algorithm. Frequent Pattern Growth (FP-Growth) is one alternative algorithm that can be used to determine the set of data that most often appears (frequent itemset) in a data set (Samuel, 2008).
Main Stages
The FP-Tree conditional generation stage,
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