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Bhatt U.Y.,Parul Institute of Technology | Patel P.A.,Parul Institute of Technology
Proceedings of 2015 IEEE 9th International Conference on Intelligent Systems and Control, ISCO 2015 | Year: 2015

Rare association rule mining provides useful information from large database. Traditional association mining techniques generate frequent rules based on frequent itemsets with reference to user defined: minimum support threshold and minimum confidence threshold. It is known as support-confidence framework. As many of generated rules are of no use, further analysis is essential to find interesting Rules. Rare association rule contains Rare Items. Rare Association Rules represents unpredictable or unknown associations, so that it becomes more interesting than frequent association rule mining. The main goal of rare association rule mining is to discover relationships among set of items in a database that occurs uncommonly. We have proposed a Maximum Constraint based method for generating rare association rule with tree structure. Tentative results show that MCRP-Tree takes less time for rule generation compared to the existing algorithm as well as it finds more interesting rare items. © 2015 IEEE.

Kothari A.A.,Parul Institute of Technology | Patel W.D.,Parul Institute of Technology
Proceedings - 2015 5th International Conference on Communication Systems and Network Technologies, CSNT 2015 | Year: 2015

Contexts and aspects have been distinguished as the significant factors in fabricating recommender systems. Most recommender systems aim at utilizing either non-contextual preferences or contextual preferences distinctly, while very few endeavors have been made to identify the significance of both. Hence an attempt has been made to study the influence of both, users' context dependent and context independent preferences in the single recommender system. In this case, accuracy has always been a challenge. Therefore, there exists a need for such a classification technique which can be commonly applied to both types of preferences that helps enhance the accuracy of the retrieved results. For this purpose, use of a standard Machine learning technique well known as Support Vector Machines was proposed. The idea behind using Support vector machines is to split the data in an optimal way and classify the data precisely to aid prediction purpose. For generating recommendations, these context-dependent preferences are further combined with users' context-independent preferences. Finally this technique is applied on a real-life dataset to demonstrate that our method is proficient in dealing with contextual preferences of users and well classify them to achieve better recommendation accuracy than the relative works. © 2015 IEEE.

Pilavare M.S.,Parul Institute of Technology | Desai A.,Parul Institute of Technology
ICIIECS 2015 - 2015 IEEE International Conference on Innovations in Information, Embedded and Communication Systems | Year: 2015

As cloud computing is having connected via network with servers so there are so many issues are there to be solved. Load balancing is the main issue over the cloud to be resolved. Various techniques are used to improve the load balancing in cloud computing. Among them various techniques are outperformed by the Genetic Algorithm. The GA uses random selection of processors as the input to it and then processes. Previously it is taken as the processors and jobs are having same priority but that is not actual case. So to improve the efficiency of GA the input processors are given first to the priority algorithm that is Logarithmic Least Square Matrix that is proposed here. The problem of being idle and starvation is taken under observation to resolve them by the proposed algorithm. © 2015 IEEE.

Mistry B.R.,Parul Institute of Technology | Desai A.,Parul Institute of Technology
ICIIECS 2015 - 2015 IEEE International Conference on Innovations in Information, Embedded and Communication Systems | Year: 2015

Association rule mining is a powerful model of data mining used for finding hidden patterns in large databases. The challenges of data mining is to secure the confidentiality of sensitive patterns when releasing database of third parties. Privacy Preserving in this paper is used as hide association rule. Association rule hiding algorithm sanitize database such that certain sensitive association rule cannot be discovered through Association rule mining techniques. There are various approach this describe in this paper but used the Heuristic approach in Data Distortion Technique. The proposed algorithm is the extension of MDSRRC algorithm, which hides multiple R.H.S items. In Proposed work MDSRRC algorithm works on the distributed database. We will show experimental results in comparisons with MDSRRC algorithm in single database and MDSRRC algorithm in distributed database. © 2015 IEEE.

Chauhan R.,Parul Institute of Technology | Patel P.A.,Parul Institute of Technology
Asian Journal of Information Technology | Year: 2014

Traditional query processors generate full, accurate queiy results, either in batch or in pipelined fashion. Researchers reviewed that this strict model is too rigid for queries over distributed data sources. For executing such query, space is required in main memory to accommodate the data for queiy processing. When main memory gets full then some data must be moved to disk for further processing as part of flushing. An optimal strategy should be used to remove the victim data from memoiy to the disk. Victim data must be selected on the basis of its contribution in future queiy processing. In this study various join algorithms and flushing techniques used in online environment is reviewed. © Medwell Journals, 2014.

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