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SECURE SYSTEM FOR DATA MINING USING RANDOM DECISION TREE AND ASSOCIATION RULES ANAND BHABAT Computer Engineering Department, Smt. Kashibai Navale College of Engineering, Pune, India. E-mail: [email protected] Abstract - In several data processing methodologies because the knowledge becomes a lot of and a lot of to store and handle in a very single machine. Therefore as an answer to the present, distributed paradigm could be an appropriate situation. By making use of random decision trees to receive distributed information within the network it becomes simple to handle the situation. However within the distributed network maintaining privacy of the information becomes a difficult job. Most of the association rules are getting therefore significant for big datasets. Therefore planned system introduces a thought of acting horizontal and vertical association victimization Apriori and Éclat rule severally within the distributed paradigm. Once more privacy is usually an enormous concern within the distributed network that is with success handled by the reverse circle cipher technique. Keywords - Random decision tree, Apriori algorithm, Éclat Algorithm, Reverse Circle Cipher Cryptography. In this paper we tend to introduce a completely unique approach of mining association rules that enhance the standard of strip-mined rules by coupling Eclat rule with comparative vertical power set. In our approach we tend to attempt to justify the potency of the Eclat rule over the Apriori rule on huge information sets by imposing mining rules victimization Apriori rule on an equivalent data set as that of Éclat association rule. In our approach we tend to create use of java XQuery to extract the information from XML files and store them in information. presently this extracted information are going to be preprocessed using varied steps like removing stop words from dataset, stemming the word, Tokenization, TF-IDF carried by Shannon info gain and top term identification. The ultimate preprocessed information provides a essential term from the information document, on which, imposing a comparative power set on vertical frequent patterns can lead to subsequent information. Further, it will be processed to turn out the economical association rule than Apriori by transmission take a look at on support and confidence given by the user. I. INTRODUCTION Most widespread job in data mining is a classification of data that automatically forecast the class for an unseen instance as precisely as possible. While in single label classification that assigns each rule as a classification has been widely used as a most obvious label, moreover discovery of all association rule is another important task in data mining. It has been depicted that classification and association rule are similar, where association rule can analyze any given attribute in the data. Wherein, classification there is only one answer to guess in classification. At present prediction of multiple labels using associative classification technique are not suitable using rules, since only one class label is associated with each derived rule. Moreover, multi-label classification is often useful in practice. Taking an example, of document that consist of two class labels namely “Japan” and “Earthquake”, assuming that the document is linked 50 times with the “Japan” and 48 times with the “Earthquake” label, therefore the number of times the document will be appearing in the training data is 98. Traditionally it is seen that, CBA which is associative technique generates the rule associated with the “Japan” label simply because it largely relate with the document, and discards the other rule. Nonetheless, we can generate other rule since it build up a useful knowledge in having a large representation set in training data, and thus could participate in classification of document. The rest of the paper is organized as follows. Section 2 discusses some related work and section 3 presents the design of our approach. The details of the results and some discussions we have conducted on this approach are presented in section 4 as Results and Discussions. Sections 5 provide hints of some extension of our approach as future work and conclusion. Rule discovery mining can be carried out using any association algorithms, required that feature selection of the data is essential. Accurate features (terms) selection can accelerate the task more perfectly. Usually the features that enhance the performance of text classification are term frequency, essential words, pruning and clustering. II. DETAILED PROPOSED SYSTEM In this section, we describe the approach of enriching process of rule mining for XML data using Eclat with RDT . The step followed by our proposed system is described as shown in Fig. 1. Proceedings of 40th IRF International Conference, 11th October 2015, Pune, India, ISBN: 978-93-85832-16-1 85 Secure System for Data Mining Using Random Decision Tree and Association Rules Step 1: In this step, documents get selected randomly from the distributed network for further processing by the use of random decision tree framework. Step 2: In this step, an XML file data is been extracted using java XQuery process and then it is been saved in the Database. Step 3: This is the step where all the XML data stored in DB are preprocessing by the following four main activities: Sentence Segmentation, Tokenization, Removing Stop Word, and Word Stemming. Sentence segmentation is boundary detection and separating source text into sentence. Tokenization is separating the input text into individual words. Next, Removing Stop Words, stop words are the words which appear frequently in the text but provide less meaning in identifying the important content of the document such as ‘a’, ‘an’, ‘the’, etc.. The last step for preprocessing is Word Stemming; Word stemming is the process of removing prefixes and suffixes of each word forming the structure. Important words are calculated based on IGR as follows: IGR(C) = -∑ (| Ci | / | C |) log (| Ci | / | C |).... (2) Where Ci is the frequency of the word w in Cluster C. Step 6: The by the using the vertical intersection of the words system identifies the most obvious words for rule mining using powerset. Where all these words are extracting by the comparative recursion of the combination of the words. Step 7: Then after fetching the important words from all the documents system will perform association rule using Apriori Algorithm with the step stated below. Let T be the training data with n attributes A1, A2, …, An and C is a list of class labels. A particular value for attribute Ai will be denoted ai, and the class labels of C are denoted cj. Definition 1: An item is defined by the association of an attribute and its value (Ai, ai), or a combination of between 1 and n different attributes values, e.g. < (A1, a1)>, < (A1, a1), (A2, a2)>, (A1, a1), (A2, a2), (A3, a3)>, … etc. Definition 2: A rule r for multi-label classification is represented in the form: (Ai1 , ai1 ) ^ (Ai2 , ai2 )^...^(A1m , aim )→ci1....cim where the condition of the rule is an item and the consequent is a list of ranked class labels. Definition 3: The actual occurrence (ActOccr) of a rule r in T is the number of cases in T that match r’s condition. Definition 4: The support count (SuppCount) of r is the number of cases in T that matches r’s condition, and belong to a class ci. When the item is associated with multiple labels, there should be a different SuppCount for each label. Definition 5: A rule r passes the minimum support threshold (MinSupp) if for r, the SuppCount(r)/ |T| ≥ MinSupp, where |T| is the number of instances in T. Definition 6: A rule r passes the minimum confidence threshold (MinConf) if SuppCount(r)/ActOccr(r) ≥ MinConf. Definition 7: Any item in T that passes the MinSupp is said to be a frequent item. Step 8: In the final step proposed system will perform vertical frequent pattern mining using éclat algorithm. Figure 1: Over view of proposed approach Step 4: Term Weight The most repeated word in the document actually plays important role to identify the importance of the sentence. Then rank of the sentences can be calculated as the sum of rank of all words in that sentence. The Rank of any words wi can be finalized by the calculation of tf-idfs (Inverse document frequency ) as shown below in 1. Wi = tf x idfi=tfix log (N / ni)…… (1) Where tfi is the tern frequency of word i in the document, N is the total number of documents, and ni is number of documents in which word i occurs. Step 5: Information Gain In order to summarize each of documents in an IR result, we use Shannon’s term weighting based on formation Gain Ratio (IGR). This method extracts the similarity structure among a set of documents through a hierarchical clustering, then gives higher weights to words that contribute to III. RESULTS AND DISCUSSION To show the effectiveness of the projected system, some experiments are reported. Choosing an acceptable dataset may be a crucial and necessary step in planning rule mining system. There is no condition in data processing for the usage of the particular dataset for the analysis. Any huge data set will be serving for this purpose. Therefore to perform experiment on our system we tend to use Proceedings of 40th IRF International Conference, 11th October 2015, Pune, India, ISBN: 978-93-85832-16-1 86 Secure System for Data Mining Using Random Decision Tree and Association Rules most generalized knowledge set from the Reuters [19] that are within the xml structure. As this knowledge set is large and having nice skillfulness it offer an honest challenge to our task. the power set with multi recursion methodology to get as maximum as possible of intersection transactions. This method actually enhances the Éclat algorithm to create frequent item sets on intersection and thereby to reduce the space and time complexity efficiently. A. Practicability of System Demonstration In our proposed system the user selects the XML dataset and extracts the needed data using XQuery to store in database. After that user need to enter minimum support and confident on the basis of which he wants to extract the rules from Éclat algorithm. Then System performs the series of feature extraction methods like tf-idf and Shannon information gain system. Then by applying a power set for the intersection of the transaction data system generates the frequent item sets. Then generated frequent item sets will be tested for the minimum support and confidence to get the efficient rule. ACKNOWLEDGMENTS I would like to thank the researchers as well as publishers for making their resources available and teachers for their guidance. REFERENCES [1] [2] B. Relevant Comparisons Author [18] proposes a method of extracting the rules using Apriori over the XML data using XQuery. For maintaining balance and similarity for the comparison proposed system also uses a dataset which contains about 20 files and average of 6 transactions in each files. And each file is containing more than 12 items. Then system was tested for various support values to check its feasibility with the Apriori algorithm whose results can be shown in below figure no 2. [3] [4] [5] [6] [7] [8] [9] [10] Fig 2 .Time comparison of Apriori and Eclat Algorithms [11] It is clearly observed from the figure 2 that as the support increases the processed time of both the algorithms leaps for same value [12] CONCLUSION [14] [13] Proposed System successfully uses the random decision tree in the distributed network to identify the working nodes to extract the association rules. In the proposed approach of mining association rules system efficiently enhance the feature of Éclat algorithm with comparative power set. Comparative power set extract the maximum frequent item sets from important words which are been decided by tf-idf and Shannon information gain. Proposed system enforces [15] [16] Jaydeep Vaidya,Basit Shafiq,Wei Fan,Danish Mehmood and David Lorenzi. “Random Decision Tree Framework for Privacy Preserving Data Mining”.In IEEE transactions on dependable and secure computing, vol. 11, no. 5, september/october 2014. Benny Pinkas “Cryptographic techniques for privacypreserving data mining”. Anand Sharma and Vibha Ojha “IMPLEMENTATION OF CRYPTOGRAPHY FOR PRIVACY PRESERVING DATA MINING” P.Kamakshi , Dr.A.Vinaya Babu“Preserving Privacy and Sharing the Data in Distributed Environment using Cryptographic Technique on Perturbed data” JOURNAL OF COMPUTING, VOLUME 2, ISSUE 4, APRIL 2010, ISSN 2151/9617. Komal N. Chouragade1 , Trupti H. Gurav “A Survey on Privacy-Preserving Data Mining using Random Decision Tree” International Journal of Science and Research (IJSR). Li Liu “ Privacy Preserving Decision Tree Mining from Perturbed Data” Proceedings of the 42nd Hawaii International Conference on System Sciences – 2009. Madhusmita Sahu , Debasis Gountia , Neelamani Samal3 “ Privacy Preservation Decision Tree Based On Data Set Complementation” International Journal of Innovative Research in Computer and Communication Engineering Vol. 1, Issue 2, April 2013. Michal Konkol and Miloslav Konopík , “Named Entity Recognition for Highly Inflectional Languages: Effects of Various Lemmatization and Stemming Approaches” Named Entity Recognition for Highly Inflectional Languages. K.K. Agbele, A.O. Adesina, N.A. Azeez , A.P. Abidoye “Context-Aware Stemming Algorithm for Semantically Related Root Words” © 2012 Afr J Comp & ICT. J. B. Lovins. (1968). Development of a Stemming Algorithm, Mechanical Translation and Computational Linguistics, vol.11, no. 12, pp: 22-31. J. Dawson. (1974). Suffix removal and word conflation. ALLC Bulletin, vol. 2, no. 3, pp: 33-46. M. Porter (1980). An Algorithm for Suffix Stripping. Program, vol. 14, no. 3, pp: 130 – 137. D. Paice Chris. (1990). Another Stemmer. ACM SIGIR Forum, Volume 24, No. 3, pp: 56-61. R. Krovetz. (1993). Viewing morphology as an inference process. In Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Pittsburgh, PA, USA – June 27 th – July 01, 1993, pp: 191-202. G.E. Freund and P. Willet. (1982), ‘Online identification of word variants and arbitrary truncation searching using a string similarity measure’. Information Technology: Research and Development, vol. 1, pp: 177-187. M. Melucci and N. Orio. (2003), A novel method for stemmer generation based on hidden Markov models. Proceedings of the 12 th international conference on Information and knowledge management, New Orleans, LA, USA, Nov 03 – 08, pp:131-138. Proceedings of 40th IRF International Conference, 11th October 2015, Pune, India, ISBN: 978-93-85832-16-1 87 Secure System for Data Mining Using Random Decision Tree and Association Rules [17] M. Prasenjit, M. Mandar, K. Swapan K. Parui, K. Gobinda, M. Pabitra and D. Kalyankumar. (2007). YASS: Yet another suffix stripper. ACM Transactions on Information Systems. vol. 25, no. 4, article 18. [18] R.Porkordi,V Bhuvaneshwari, R. Rajesh and T. Amudha “An Improved Association Rule Mining Technique for Xml Data Using Xquery and Apriori Algorithm “, IEEE International Advance Computing Conference (IACC 2009) Patiala, India, 6-7 March 2009. [19] http://www.daviddlewis.com/resources/testcollections/reuter s21578/. Proceedings of 40th IRF International Conference, 11th October 2015, Pune, India, ISBN: 978-93-85832-16-1 88