CLASSIFICATION BASED ON MODIFIED MULTICLASS ASSOCIATION RULE. Varshaa.R 1, Sairam.N 2

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1 Volume 115 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu CLASSIFICATION BASED ON MODIFIED MULTICLASS ASSOCIATION RULE Varshaa.R 1, Sairam.N 2 1 M.Tech-ADC, 2 Associate Dean-IT School of Computing, SASTRA University, Thanjavur, Tamilnadu, India. Abstract: Association classification is one of the methods for discovering the relations between the variables in large database. Traditionally the Association mining algorithm will have repetitive rules and in the existing system using Apriori algorithm we have a bottleneck of validation only evaluating single rule. Hence in this document we will discuss the possible solution to overcome these conflicts. To overcome the current limitations that are prevailing over the usage of Apriori Algorithm we are proposing to replace it with the Multi label Principality which helps us in yielding better accuracy, avoiding redundancy and limited rules. The Mulilabel principality is proposed for efficient classification and for better classification accuracy.the rule is generated with the support and confidence value. The accuracy of the rule is low in single rule principality and to increase the accuracy of the rules, we are using multilabel rule principality. The Multilabel principality gives high efficient and accuracy values. This is used is finding the accurate rules in association classification. Keywords: Data mining, Classification, Association Classification, Association rule, Multilabel rules 1. Introduction Classification is an important task in data mining.the classifier is built from the training data set. It consists of database tuples and class labels. Association mining was first proposed by Agarwal.The association rules are discovered by association mining.the association mining is represented by itemsets and rules.the association rule is in the form of A C.Where A is known as the antecedent and C is known as consequent.this rule gives the relationship between the two variables A and C.The antecedent gives the value of co-occurent attributes and the consequent is the target class attributes. In data mining the essential task is to build an efficient and accurate classifier.the classification is used to build more accurate classifier with a small set of rules.when one rule is created, the data associated with that rule will be deleted from the training data set.there will be no dependency between the rules. The classified the data with an apriori algorithm. The association rules are framed with the support and confidence values [1]. If the support and confidence values are the same, we cannot predict the correct rule.a new method called principality is proposed. This method gives better classification accuracy. There will be no redundancy between the rules. The classified the data based on multilabel rules. The overlapping between rules and redundancy is avoided [2].When one rule is created, the data associated with that rule will be deleted from the training data set.this improved the accuracy of the classification.there will be no dependency between the rules.in [3]the concept of association mining is used. The associative property is used for binary operations and rearranging the parentheses in an expression that does not change the value.statistical classification is used to find the categories of the data. In [4] association rule learning is used for finding the relationship between the variables.data mining is used for new pattern discovery from the dataset. In [5]Classified the data based on frequent pattern discovery is used. It uses the apriori approach for frequent pattern discovery.the pruning strategy is used for deleting the redundant and useless rules. In [6] CBA purnes the rules with support and confidence value.the rules having the lower threshold value of support and confidence value will be deleted.the rules having low support value and highest confidence value will also be 183

2 deleted. In [7] FP-growth approach which adopts the divide and conquer approach is used.the dataset is partitioned according to divide and conquer approach.this will be like a tree structure.each node in a tree represents single item.the association classification gives better accuracy than the decision tree. In [8]the rules which are preference with effect are detected in association classification. In [9] the rules which have more confidence value and less support value is calculated and inducing model is introduced for more accurate value.this gives the accurate rules. In [10] various association classification methods are compared. The association classification is of two types single label and mutilabel. On the single label, the rule will have only one class. In the multi label, each rule will have more than two class labels. The rules having more than two classes are called as multi label classification. In the single class rule there will be more dependency and redundancy.in multi class rule the redundancy will be avoided. The single rule is generated with many numbers of rules with a single class label. The multilabel rule is generated with less number of rules, each rules have more than one class label. 2. Proposed Model In data mining association classification is an important method.using classification rules can be framed.the rules gives the relationship between the variables. In association classification the rules are framed with support and a confidence value.the rule which passes the support and confidence threshold are taken. In association classification, a principality method for better accuracy is proposed.the proposed method consist of four phases: Frequent rule items discovery, support and confidence computation, principality and multilabel rules. 2.1 Frequent Items Discovery The item-set is given with rowids,attributes and class labels.therowids consists of integers,the attributes have the items and the class label has the different classes.therowids of same items having different class labels are stored.the dataset is in the form of <rowid>,<attr1>,<attr2> and <class>.for example <attr1,v1> and <attr1,s1> in Table 1,the rowids of two set are{1,2,3,4} and{1,3,5,6,7}.the same rowids of two datasets are taken.therowids {1,3} are taken. These two rowids have the same attributes but different class. Table 1. Training dataset rowid Attr1 Attr2 Class 1 v1 s1 cl1 2 v1 s2 cl2 3 v1 s1 cl2 4 v1 s2 cl1 5 v2 s1 cl2 6 v2 s1 cl1 7 v2 s1 cl2 2.2Support and Confidence Computation Support value can be calculated by dividing the number of rules that have different class labels with total number of transactions.the confidence is calculated as rowids on the same items with different class labels.the rules are taken if it passes the support and confidence threshold. The rules with low support and confidence value are ignored and the items associated with that rule are deleted from the dataset. 2.3 Principality The association rules are calculated with the support and confidence value. If the two rules have the same support and different confidence values, then one predict which rule is the best.here the principality concept is used. The Principality is calculated with the supportand confidence value.the rule will be in the form of R c. p(r,c)=λ*conf(r)+(1-λ)comp(r)=λ* xc / x +(1- λ)* xc / c.where c is the objects in the class, x is frequency count of condition R and xc is a cooccurrence count of condition X andconsequent c.the value of is between 0 and 1.The principality gives better accuracy and efficient values. 2.4 Multilabel rules The proposed method is used for multilabel rule generations. The existing association classification gives the single rule. The mutilabel rule is 184

3 generated with principality value.the rules with highest principality value is taken. We would also like to recommend to infuse Post pruning method in future which will result in cutting down the rules further. 3. Results In association classification the rule discovery is done with an apriori approach. In an apriori approach, single rule principality gives less accuracy. Hence the multilabel principality is proposed. The proposed method gives better accuracy than the single rule principality. 3.1 Single rule Graph The single rules are generated with association classification support and confidence value. In the single rule principality there will be more number of rules and the accuracy rate is low,as shown in figure1.the x represents the principality value and frequency represents the number of rules. Figure 2. Multilabel rule principality Acknowledgments The authors wish to express their sincere thanks to the Department of Science & Technology, New Delhi, India (Project-ID:SR/FST/ETI-371/2014) and SASTRA University, Thanjavur, India for extending the infrastructural support to carry out this work. References [1] FuzanChen,YanlanWang.Principal Association Mining: An Efficient Classification Approach. Knowl Based Syst, 6:16-25, Sept Figure 1.Single rule principality 3.2 Multilabel rule Graph The mulitlabel is used to reduce the number of rules and for better accuracy.in multilabel principality,it gives better accuracy than the single rule principality.this does not need multiple database transactions.it gives a small set of rule with better accuracy as shown in figure2. 4. Conclusion Thus with the yielded resul+t we are able to prove Association classification is one among the best classification methods available and it will in turn give us a better result when used with Multi label Principality rather than with the Single Rule Principality (Apriori). Also usage of Multi label Principality helps in achieving Accuracy,overlapping between the rules, multiple database transaction and redundancy are avoided. [2] F.A. Thabtah, P.I. Cowling. A greedy classification algorithm based on association rule. Appl. Soft Comput. 7 (3): ,2007. [3] F. Thabtah. A review of associative classification mining, KnowlEng Rev,22(1):37-65,Mar-2007 [4] Y. Lan, D. Janssens, G. Chen, G.Wets. Improving associative classification by incorporating novel interestingness measures. Expert Syst Appl,31: ,Jul [5] C.M. Bishop. Pattern Recognition and Machine Learning. Springer, New York,2006. [6] X. Zhang, G. Chen, Q.Wei. Building a highlycompact and accurate associativeclassifier. Appl. Intell, 34 (1):74-86, Feb

4 [7] W. Li, J. Han, J. Pei, CMAR: Accurate and efficient classification based onmultiple-class association rule. Proceedings 2001 IEEE International Conference on Data Mining, San Jose, CA, pp , [8] F. Thabtah. Rule preference effect in associative classification mining. J.Info. Knowl. Manage,5 (1):1 7, [9] Cendowska. PRISM: an algorithm for inducing modular rules. Int. J.Man-Mach. Stud,27(4): , Oct [10] R. Kulothungan, M. Manikandan, K. Murugananthan, P. Praveen, Implement The Banking Security Based Key Exchage Protocal And Keystroke Authentication, International Innovative Research Journal of Engineering and Technology, 30-33, March [11] H. Patel, D. Patel: A comparative study on various datamining algorithms with special reference to crop yield prediction. Indian Journal of Science and Technology,

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