 Open Access
 Total Downloads : 512
 Authors : Nikhil Jain, Vishal Sharma
 Paper ID : IJERTV1IS8383
 Volume & Issue : Volume 01, Issue 08 (October 2012)
 Published (First Online): 29102012
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Distance Weight Parity Optimization of Association Rule Mining With Genetic Algorithm
Nikhil Jain & Vishal Sharma Department of Computer Science and Engineering Jawaharlal Institute of Technology, Khargone(M.P.)
Abstract Association rule mining is the method for finding interesting relations between variables in database. In this paper we focus on the problem of mining association rules. Association rule mining suffered by the generation of negative rules and superiority problem. To overcome this problem we use genetic algorithm which give us the optimized association rules. By using Apriori algorithm we prune the candidate itemsets that cant meet the minimum support count.
Keywords Association rule mining; support; confidence

Introduction
Data mining is play very important role in current growing rate of internet data. It is also a vital field of research in the field of pattern extraction and gathering of information on given database. The task of data mining is to extract useful knowledge for human users from a database. Here as the application of evolutionary computation to data mining is not always easy due to its heavy computation load especially in the case of a large database [6].

Association Rule Mining
Association rule mining is to find out association rules that satisfy the predefined minimum support and confidence from a given database. The problem is usually decomposed into two sub problems. One is to find those item sets whose occurrences exceed a predefined threshold in the database; those item sets are called frequent or large item sets. The second problem is to generate association rules from those large item sets with the constraints of minimal confidence[1]. Suppose one of the large item sets is Lk, Lk = {I1, I2 Ik}, association rules with this item sets are generated in the following way: the first rule is {I1, I2 Ik1} {Ik}, by checking the confidence this rule can be determined as interesting or not. Then other rule are generated by Deleting the last items in the antecedent and inserting it to the consequent, further the confidences of the new rules are checked to determine the interestingness of them. Those processes iterated until the antecedent becomes empty. Since the second sub problem is quite straight forward, most of the researches focus on the first sub problem. The first sub problem can be further divided into two subproblems: candidate large item sets generation process and frequent item sets generation process. We call those item sets whose support exceed the support threshold as large or frequent itemsets, those item sets that are expected or have the hope to be large or frequent are called candidate item sets. In many cases, the algorithms generate an extremely large number of association rules, often in thousands or even millions. Further, the association rules are sometimes very large[3]. It is nearly impossible for the end users to comprehend or validate such large number of complex association rules, thereby limiting the usefulness of the data mining results. Several strategies have been proposed to reduce the number of association rules, such as generating only
interesting rules, generating only no redundant rules, or generating only those rules satisfying certain other criteria such as coverage, leverage, lift or strength.

Apriori Algorithm
The work of the Apriori Algorithm is to find associations between different sets of data. Each set of data has a number of items and is called a transaction. The result of Apriori is sets of rules that tell us how often items are contained in sets of data. It is based on the breadth first search algorithm which is moving in upward direction. The first pass of the algorithm counts item occurrences to find the frequent itemsets .A subsequent pass k consists of two steps. First the frequent itemsets Lk1 found in the (k1)th pass are used to generate candidate itemsets Ck. Next the database is scanned and the support of candidates in Ck is counted. The set of candidate itemsets is a pruning process to ensure that all the subsets of candidate sets are already known to be frequent itemsets. The pruning process and the candidate generation process is the part of the algorithm.
Candidate Generation is the process in which Lk1 the set of all frequent (k1) itemsets, we want to generate superset of the set of all frequent kitemsets.the motive behind the apriori candidate generation is that if an item set X has a minimum support, so do all subsets of X. The Pruning steps eliminates the extension of (k1)itemsets which are not found to be frequent from being considered for counting support.
The Apriori algorithm uses these two functions at every iteration.

Genetic Algorithm
Genetic Algorithm (GA), first introduced by John Holland in the early seventies, is the powerful stochastic algorithm based on the principles of natural selection and natural genetics, which has been quite successfully, applied in machine learning and optimization problems. To solve a Problem, a GA maintains a population of individuals (also called strings or chromosomes) and probabilistically modifies the population by some genetic, operators such as selection, crossover and mutation, with the intent of seeking a near optimal solution to the problem. Coding to Strings in GA [4, 5], each individual in a population is usually coded as coded as a fixedlength binary string. The length of the string depends on the domain of the parameters and the required precision.

Initial Population
The initial process is quite simple. Create a population of individuals, where individual in a population is a binary string with a fixedlength, and every bit of the binary string is initialized randomly.

Evaluation
In each generation for which the GA is run, each individual in the population is evaluated against the unknown environment. The fitness values are associated with the values of objective function.

Genetic Operators
To perform genetic operators, must select individuals in the population to be operated on .The selection strategy is chiefly based on the fitness level of the individuals actually presented in the population. There are many different selection strategies based on fitness. The most popular is the fitness proportionate selection. After a new population is formed by selection process, some members of the new populations undergo transformations by means of genetic operators to form new solutions (a recombination step). Because of intuitive similarities, we only employ during the recombination phase of the GA three basic operators: reproduction, crossover and mutation, which are controlled by the parameter rp, cp and mp(reproduction probability, crossover probability and Mutation probability), respectively. Let us illustrate
these three genetic operators. As an individual is selected, reproduction operators only copy it from the current population into the new population (i.e., the new generation) without alternation. The crossover operator starts with two selected individuals and then the crossover point (an integer between 1 and L1, where L is the length of strings) is selected randomly. Assuming the two parental individuals are x1 and x2, and the crossover point is 5 (L=15). If
X1= (111011001001110) X2= (011100100111100)
Then the two resulting offspring are X1= (000100110110001)
X2= (011101011000011)
The third genetic operator, mutation, introduces random changes in structures in the population, and it may ocasionally have beneficial results: escaping from a local optimum. In our GA, mutation is just to negate every bit of the strings, i.e., changes a 1 to 0 and vice versa, with probability pm.


Proposed Methodology
The proposed algorithm is a combination of support weight value and near distance of superior candidate key and parity based selection of rule based on group value of rule. Support weight key is a vector value given by the transaction data set and plays a role of rule selection on the base of genetic parity order. The support value passes as a vector for finding a near distance between superior candidate key. After finding a superior candidate key the nearest distance divide into two classes, one class take a higher odder value and another class gain lower value for rule generation process. The process of selection of class also reduces the passes of data set. After finding a class of lower and higher of given support value compare the value of distance weight vector. Here distance weight vector work as a fitness function for selection process of genetic algorithm. Here we present steps of process of algorithm step by step
Steps of algorithm

Select data set

Put value of support and confidence

Start scanning of transaction table

Count frequent items

Generate frequent itemsets

Check the transaction set of data is null

Put the value of support as weight

Compute the distance with Euclidean distance formula

Generate distance vector value for selection process

Assign random parity of each group of selected vector

Initialized a population set (t=1)

Compare the value of distance vector with population set

Selected value of parity arrange by distance weight factor.

If value of support greater than vector value of parity odder.

Processed for encoded of data

Encoding format is binary

After encoding offspring are performed

Set the value of probability for mutation and the value of probability is 0.007.

Set of rule is generated.

Check superiority of rule in set of parity group

If rule is not superior go to selection process

Else optimized rule is generated.

Exit


CONCLUSION
We have dealt with association rule mining problem of finding optimized association rules. The frequent itemsets are found by Apriori algorithm and after that Genetic algorithm has been applied on frequent itemsets to generate the optimized rules.
in this paper the authors have tried to use the enormous robustness of GAs in mining the Association Rules. The results generated when the technique applied on the synthetic database, includes the desired rules, i.e. rules containing the negation of the attributes as well as the general rules evolved from the Association Rule Mining. The authors believe that the toolkit can also handle other databases, after minor modifications. As for future work, the authors are currently working on the complexity reduction of Genetic Algorithms by using distributed computing.
References

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