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Generate Frequent Itemsets with Top Down Apriori Algorithm Using Map Reduce

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In data mining, Association rule tasks of descriptive technique which can be defined as discovering meaningful patterns from the large collection of data. Mining frequent itemset is very fundamental part of association rule mining. Many algorithms have been proposed from last many decades including horizontal layout based techniques, based techniques and projected layout based techniques. But most of the techniques suffer from repeated database scan, Candidate generation (Apriori Algorithms), memory consumption problem and many more for mining frequent patterns. As in retailer industry, many transactional databases contain the same set of transactions many times, to apply this thought, thesis presents an improved Apriori algorithm that guarantees the better performance than classical Apriori algorithm.

Data mining is the main part of KDD. Data mining normally involves four classes of the task; classification, clustering, regression, and association rule learning. Data mining refers to discover knowledge in enormous amounts of data. It is a precise discipline that is concerned with analyzing observational data sets with the objective of finding unsuspected relationships and produces a review of the data in novel ways that the owner can understand and use.

Data mining as a field of study involves the integration of ideas from many domains rather than a pure discipline the four main disciplines, which are contributing to data mining include:

Statistics: it can make for measuring the given data, estimating probabilities and many other tasks (e. g. linear regression).

Machine learning: it provides algorithms for inducing knowledge from given data (eg. SVM).

Data management and databases: in view of the fact that data mining deals with the huge size of data, an efficient way of accessing and maintaining data is needed.

Artificial intelligence: it contributes to tasks involving knowledge encoding or search techniques (e. g. neural networks).

It is fundamentally important to declare that the prime key to understand and realize the data mining technology is the ability to make different between data mining, operations, Applications and techniques, as shown in Figure 2

One of the most well known and popular data mining techniques is the Association rules or frequent itemsets mining algorithm. Because of its important applicability, many revised algorithms have been introduced since then, and Association rule mining is still a widely researched area. Many variations done on the frequent pattern-mining algorithm of Apriori was discussed in this article.

AIS algorithm in which generates candidate item sets on-the-fly during each pass of the database scan. Large item sets from preceding pass are checked if they were presented in the current transaction. Therefore extending existing item sets created new item sets. This algorithm turns out to be ineffective because it generates too many candidate item sets. It requires more space and at the same time, this algorithm requires too many passes over the whole database and also it generates rules with one consequent item.

The techniques for discovering association rules from the data have conventionally focused on identifying relationships between items telling me feature of human behavior, usually trade behavior for determining items that customers buy together. All rules of this type describe a particular local pattern. The group of association rules can be simply interpreted and communicated. The association rule xyhas supports in D if the probability of a transaction in D contains both X and Y is s.

The task of mining association rules is to find all the association rules whose support is larger than a minimum support threshold and whose confidence is larger than a minimum confidence threshold. These rules are called the strong association rules.

Apriori employs an iterative approach known as a level-wise search, where k-itemsets are used to explore (k+1)-itemsets.

First, the set of frequent 1-itemsets is found. This set is denoted L1.L1is used to find L2, the set of frequent 2-itemsets, which is used to find L3, and so on, until no more frequent k-itemsets can be found. The finding of each Lkrequires one full scan of the database. In order to find all the frequent itemsets, the algorithm adopted the recursive method. The main idea is as follows:

Apriori Algorithm (Itemset[]).

{ L1 = {large 1-itemsets};

for (k=2; Lk-1F; k++) do

{ Ck=Apriori-gen (Lk-1);

{ Ct=subset (Ck, t);

// get the subsets of t that are candidates for each candidates c Ct do

c.count++;}

Lk={cCk |c.count=minsup} } Return=kLk;}

This new proposed method uses a large amount of item set and reduces the number of database scan. This approach takes less time than apriori algorithm. The MAP-REDUCE(HADOOP) Apriori algorithm which reduces unnecessary database scan.

Pseudo Code of Proposed Method.

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 D[i][j]==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;}

Algorithm Map_Reduce(count[ ],D[ ][ ])

{i=1;

while(i<No of Transaction) { MAPER(i,no_of_transactions/2)

MAPER(no_of_transaction/2+1,no_of_transaction)

REDUCER(i,no_of_transactions/2)

REDUCER(no_of_transaction/2+1,no_of_transaction) } return Association Rule }

In this paper, we measured the following factors for creating our new idea, which is the time and the no of iteration, these factors, are affected by the approach for finding the frequent itemsets. Work has been done to develop an algorithm which is an improvement over Apriori with using an approach of improved Apriori algorithm for a transactional database. According to our clarification, the performances of the algorithms strongly depend on the support levels and the features of the datasets (the nature and the size of the datasets). Therefore we employed It in our scheme to guarantee the time saving and reduce the no of iteration Thus this algorithm produces frequent item sets completely. Thus it saves much time and considered as an efficient method as proved by the results.

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Generate Frequent Itemsets with Top Down Apriori Algorithm Using Map Reduce. (2018, May 06). GradesFixer. Retrieved July 24, 2021, from https://gradesfixer.com/free-essay-examples/generate-frequent-item-sets-with-top-down-apriori-algorithm-using-map-reduce/
“Generate Frequent Itemsets with Top Down Apriori Algorithm Using Map Reduce.” GradesFixer, 06 May 2018, gradesfixer.com/free-essay-examples/generate-frequent-item-sets-with-top-down-apriori-algorithm-using-map-reduce/
Generate Frequent Itemsets with Top Down Apriori Algorithm Using Map Reduce. [online]. Available at: <https://gradesfixer.com/free-essay-examples/generate-frequent-item-sets-with-top-down-apriori-algorithm-using-map-reduce/> [Accessed 24 Jul. 2021].
Generate Frequent Itemsets with Top Down Apriori Algorithm Using Map Reduce [Internet]. GradesFixer. 2018 May 06 [cited 2021 Jul 24]. Available from: https://gradesfixer.com/free-essay-examples/generate-frequent-item-sets-with-top-down-apriori-algorithm-using-map-reduce/
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