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 Total Downloads : 1414
 Authors : Rupali, Gaurav Gupta
 Paper ID : IJERTV2IS70053
 Volume & Issue : Volume 02, Issue 07 (July 2013)
 Published (First Online): 01072013
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Apriori Based Algorithms And Their Comparisons
Rupali
Student, Computer Engineering University College of Engineering Punjabi University
Patiala, India
Gaurav Gupta Department of Computer Engineering
University College of Engineering Punjabi University
Patiala, India
Abstract Mining frequent itemsets from the large transactional database is a very critical and important task. The first algorithm for mining all frequent itemsets and strong association rules was the AIS algorithm by [2]. Shortly after that, the algorithm was improved and renamed Apriori. Apriori algorithm is the most classical and important algorithm for mining frequent itemsets. This paper aims to presents a basic Concepts of some of the algorithms (Direct Hashing and Pruning (DHP), Partitioning, Sampling, Dynamic Itemset Counting (DIC), Improved Apriori algorithm) based upon the Apriori like algorithm for mining the frequent item sets along with their capabilities and comparisons.
Keywords Data mining, Apriori variations, Frequent Itemsets.

INTRODUCTION
Frequent item set mining is one of the most important and common topic of research for association rule mining in data mining research area. As an association rule mining is defined as the relation between various itemsets. Association rule mining takes part in pattern discovery techniques in
knowledge discovery and data mining (KDD). As performance of association rule mining is depends upon the frequent itemsets mining, thus is necessary to mine frequent item set efficiently.
The process of extracting association rule mining consists of two parts firstly, mine all frequent itemsets pattern each of these pattern should satisfy the minimum support threshold. Once these entire frequent patterns are mined, then only second phase of mining
i.e. association rules are produced from these frequent itemsets. These association rules must satisfy the minimum support and minimum confidence. This minimum support and confidence should be defined by the user. In this way the generation of association rule is largely depends upon the generation of the frequent items in the first phase. [1, 2, 3]
A large number of algorithms with different mining efficiencies were proposed by many researchers for generation of frequent itemsets. Any algorithm should find the same set of rules though their computational efficiencies and memory requirements may be different. The best known mining algorithm is Apriori algorithm.
The further organisation of this paper is as follows. In Section 2, we briefly define the problem statement for finding the frequent itemsets from transactional database. Section
3 defines the classic Apriori algorithm. Sections 4 define the existing techniques based upon the Apriori algorithm. Section 5 defines their comparisons. Section 6 concludes the paper.

PROBLEM STATEMENT
The problem of mining association rules over market basket analysis was introduced in [4] .i.e. finding associations between the items that are present in the transaction from the database. The database may be from any retail shop, medical or from any other applications [5]. As defined in [2] the problem is stated as follows:
Let I= i1, i2 . . . im be a set of literals, called items and m is considered the dimensionality of the problem. Let D be a set of transactions, where each transaction T is a set of items such that T I. A unique identifier TID is given to each transaction. A transaction T is said to contain X, a set of items in I. X T An association rule is an implication of the form X Y , where X
I , Y I, and X Y = . An itemset X is said to be large or frequent if its support s is greater or equal than a given minimum support threshold . An itemset X satisfies a constraint C if and only if C (X) is true. The rule X Y has a support s in the transaction set D if s% of the transactions in D contain X
Y. In other words, the support of the rule is the probability that X and Y hold together among all the possible presented cases. It is said that the rule X Y holds in the
transaction set D with confidence c if c% of transactions in D that contain X also contain Y . In other words, the confidence of the rule is the conditional probability that the consequent Y is true under the condition of the antecedent X. The problem of discovering all association rules from a set of transactions D consists of generating the rules that have a support and confidence greater than a given threshold. These rules are called Strong Rules. This association mining task can be broken into two steps:

Finding the frequent kitemset from the large database.

Generate the association rule from these frequent item sets.

APRIORI ALGORITHM
The first algorithm for mining all frequent itemsets and strong association rules was the AIS algorithm by [1]. Shortly after that, the algorithm was improved and renamed Apriori. Apriori algorithm is, the most classical and important algorithm for mining frequent itemsets. Apriori is used to find all frequent itemsets in a given database DB. The key idea of Apriori algorithm is to make multiple passes over the database. It employs an iterative approach known as a breadthfirst search (levelwise search) through the search space, where kitemsets are used to explore (k+1)itemsets.
The working of Apriori algorithm is fairly depends upon the Apriori property which states that All nonempty subsets of a frequent itemsets must be frequent [6]. It also described the anti monotonic property which says if the system cannot pass the minimum support test, all its
supersets will fail to pass the test [6, 1]. Therefore if the one set is infrequent then all its supersets are also frequent and vice versa. This property is used to prune the infrequent candidate elements.
It is no doubt that Apriori algorithm successfully finds the frequent elements from the database. But as the dimensionality of the database increase with the number of items then:

More search space is needed and I/O cost will increase.

Number of database scan is increased thus candidate generation will increase results in increase in computational cost.
Therefore many variations have been takes place in the Apriori algorithm to minimize the above limitations arises due to increase in size of database. These subsequently proposed algorithms adopt similar database scan level by level as in Apriori algorithm, while the methods of candidate generation and pruning, support counting and candidate representation may differ. The algorithms improve the Apriori algorithms by:

Reduce passes of transaction database scans

Shrink number of candidates

Facilitate support counting of candidates



APRIORI ALGORITHM
VARIATIONS

Direct Hashing and Pruning (DHP):
It is absorbed that reducing the candidate
items from the database is one of the important task for increasing the efficiency. Thus a DHP technique was proposed [7] to reduce the number of candidates in the early passes Ck for k>1 and thus the size of database. In this method support is counted by mapping the items from the candidate list into the buckets which is divided according to support known as Hash table structure. As the new itemset is encountered if item exist earlier then increase the bucket count else insert into new bucket. Thus in the end the bucket whose support count is less the minimum support is removed from te candidate set.
In this way it reduce the generation of candidate sets in the earlier stages but as the level increase the size of bucket also increase thus difficult to manage hash table as well candidate set.

Partitioning Algorithm:
Partitioning algorithm [8] is based to find the frequent elements on the basis partitioning of database in n parts. It overcomes the memory problem for large database which do not fit into main memory because small parts of database easily fit into main memory. This algorithm divides into two passes,
Figure 1: Mining Frequent itemsets using Partition algorithm [9]
It should be noted that if the minimum support for transactions in whole database is min_sup then the minimum support for partitioned transactions is minsup number of transaction in that partition.
A local frequent itemset may or may not be frequent with respect to the entire database thus any itemset which is potentially frequent must include in any one of the frequent partition.
As this algorithm able to reduce the database scan for generating frequent itemsets but in some cases, the time needed to compute the frequency of candidate generates in each partitions is greater than the database scan thus results in increased computational cost.

Sampling Algorithm:
This algorithm [10] is used to overcome the limitation of I/O overhead by not considering the whole database for checking the frequency. It is just based in the idea to pick a random sample of itemset R from the
database instead of whole database D. The sample is picked in such a way that whole sample is accommodated in the main memory. In this way we try to find the frequent elements for the sample only and there is chance to miss the global frequent elements in that sample therefore lower threshold support is used instead of actual minimum support to find the frequent elements local to sample. In the best case only one pass is needed to find all frequent elements if all the elements included in sample and if elements missed in sample then second pass are needed to find the itemsets missed in first pass or in sample [11].
Thus this approach is beneficial if efficiency is more important than the accuracy because this approach gives the result in very less scan or time and overcome the limitation of memory consumption arises due to generation of large amount of datasets but results are not as much accurate.

Dynamic Itemset Counting (DIC):
This algorithm [12] also used to reduce the number of database scan. It is based upon the downward disclosure property in which adds the candidate itemsets at different point of time during the scan. In this dynamic blocks are formed from the database marked by start points and unlike the previous techniques of Apriori it dynamically changes the sets of candidates during the database scan. Unlike the Apriori it cannot start the next level scan at the end of first level scan, it start the scan by starting label attached to each dynamic partition of candidate sets.
In this way it reduce the database scan for finding the frequent itemsets by just adding the new candidate at any point of time during the run time. But it generates the large number of candidates and computing their frequencies are the bottleneck of performance while the database scans only take a small part of runtime.


COMPARISON OF ALGORITHMS

CONCLUSION
In this paper we studied the basic algorithm
i.e. Apriori, to mine the frequent item sets.
Apriori algorithm is, the most classical and important algorithm for mining frequent itemsets. Apriori is used to find all frequent itemsets in a given database DB. Apriori algorithm is associated with certain limitations of large database scans. Thus variations of Apriori come into existence. The various variations we discussed are DHP, Partition, DIC and Sampling algorithms. We also studied their comparisons.
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