CHAPTER while(i

CHAPTER
3

Proposed
solution

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3.1System
overview

This chapter includes element of implementation of the new approach
to extract the frequent
item
sets with example.
The major objective of the
research
is to develop and
propose a new idea for
mining the association rules
out of transactional dataset. The proposed method is based on Improved Apriori approach.
The proposed method is more efficient than classical Apriori algorithm. To
achieve the research
objective successfully, a series of
sequence
progresses and
analysis steps have
been adopted.  Figure3.1 depicts
the method to mine frequent
item sets from the transactional dataset using
the new
method.

Figure3.1:Methodusedtominefrequent
itemsets

 

 

 

3.2 Algorithm study

3.2.1 Improved Apriori Algorithm

Algorithm
Apriori_MapReduce_Partitioning(D ,supp)
{
                // D—Input dataset
                //supp — Minimum support
 
   no_transaction =
calculate_transaction(D)
   no_item =  calculate_item(D);
for i=1 to no_of_transaction do
                {
                                for j=1 to no_of_items do
                                {
                                                if  Dij==1 then
                                                {
                                                                countj++;
                                                }
}
}
for j=1 to no_of_item  do
{  

                                if(countj>
sup)
                                {
                                                 add_item (j);
                                }
}
  frequent_items=Map_Reduce(D); // calling
Map Reduce algorithm
  return frequent_items;
}

Using top down approach usually uses the improved Apriori
algorithm for association mining technique.  The top down Apriori algorithms requirements
to large frequent item sets and generates frequent candidate item sets. The improved Apriori algorithms, which reduce unnecessary
database, scan. This algorithm is useful for large amount of item set.  Therefore, improved top down algorithm uses less space, less number of iteration.

 

 

 

Algorithm
calculate_transaction (D)
{
                no_of_transaction
= 0;
                s=space;
                while(i

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