Background/Introduction being snatch by data miner. Second setting
rule learning is a technique to discover the relations between variables in huge
database. It used to form horizontally distributed databases located in centralized
database. Association rule is found based on low speed of data retrieval that
caused by centralized database server served for all side of database
simultaneously. Thus, “association rule helps to group the data into small
fragments and stored at distinct site of computer”. It also brings benefit of
improving availability of the database to 24/7h and maintain the normalisation
of the database simultaneously 1. The most usefulness of the technique is to
speed up the data retrieval and reduce timing required to load into database.
Mining association rule is an advanced technique to measure the rule of
interestingness. Algorithm will be used for the technique is Apriori algorithm which
comply with the principle of all of the subset of a frequent itemset in
database must be frequent and could use if-then statement to calculate the
frequent itemset with corresponding subsets. Association rule learning is an alternative
protocol improve simplicity, efficiency and privacy of the subset at which enhances
security of computation of the subset 2.
distributed database could use association rule to subdivide the data into four
modules. Huge data will be subdividing into user module, administrator module,
association rule and Apriori Algorithm. In User module, there have two setting
to be considered, that are data owner and data miner could not share same data
and several parties share same data. First setting applies data perturbation to
hide and protect data from being snatch by data miner. Second setting requires
data mining to have protection to data from other parties. Administrator module
is a module for admin to view user details based on user processing details. Third
module is association rules that applied to horizontally distributed database to
identify relations between data based on if/then statement. Last module,
Apriori algorithm make use for finding association between data fragments especially
databases that containing transaction.
There are a few references that has been reviewed and
used in this article.
Sayad Shujaubuddin Sameer
The paper discussing about the security applied on
sensitive data using association rule is very importance in aspect data mining
and other learning techniques. In the future, privacy will emphasize on data mining due to
productive of development of data mining.
Rakesh Agrawal and Ramkrishnan Shrikant
The paper review on fast algorithm could be applied on
distribution database for mining association rules purpose on various computer
within a network 3. Bar-code technology or known as basket data able to collect
and store huge data by retail organizations through the rules.
M.saraaswati and N. Kowsalya
The paper discussing on privacy preserving and data
secure mining of association rule in distributed rule 1. Apriori algorithm already
applied on most of parallel and distributed ARM algorithm but directly apply
Apriori algorithm won’t obviously improve the performance of distributed ARM.
The result outcome/contribution
Association rule should be able to
improve the performance of data distribution in the aspect of computation time.
Testing should be conduct with constant of size of data with dynamic elements
within each data. The output should have constant changing of estimated time
inversely proportional to element size.
Association rules applied in
horizontally distributed databases can improve the privacy and efficiency compared
to current leading protocol. Association of algorithm with association rule is
a high secure multi-party protocol that could use to computing the subset of
data that involve various parties. The module also tests for performance of
data held by different person. Problems on data performance at different side just
can be found when the players involved is more than two.