Volume-3 , Issue-7 , Jul - 2015 Go Back

A digital watermark is a kind of marker convertly embedded in a noise tolerant signal such as audio or image data. It is typically used to identify ownership of the copyright of such signal. Watermarking is a process of hiding digital information in a carrier signal, the hidden information should but doesnot need to contain a relation to the carrier signal. Digital watermarks may be used to verify the authenticity or integrity of the carrier signal or to show the identity of its owners. It is prominently used for tracing copyright infringements and for bank note authentication. Traditional watermarks may be applied to visible media such as images or video, whereas in digital watermarking, the signal may be audio, pictures, video, texts or 3D models. A signal may carry several different watermarks at the same time.

Digitalwatermarks,Cryptography,Matlab

Dheeraj Sai D V L N, K N S Aneesh, "Image Water Marking Using Cryptography," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.171-178, 2015.

Item set mining is one of the famous data mining method in which frequent and infrequent items can be mined. Now a days, the research society has focused on the problem of infrequent itemset mining, i.e., find out the item sets which has frequency of occurrence in transactional data base is less than or equal to a maximum threshold. Discovering rare item set is more interesting than mining frequent ones. The existing system deal with the issue of discovering infrequent weighted item sets. Infrequent Weighted Itemset Miner (IWI Miner) and Minimal Infrequent Weighted Itemset Miner (MIWI Miner) algorithms are introduced for efficient IWI and Minimal IWI mining. In many real world situations, utility of item sets depends on user‘s perspective such as cost, profit or revenue which are major significance. The existing Infrequent weighted item set mining algorithms are used to find out infrequent item sets from weighted transactional database, it does not compute utility of items. So in the proposed system introduced low utility based Infrequent Weighted Itemset mining (LUIWIM) algorithm. The proposed system is used for effectively mine the low utility infrequent weighted item set according to the profit, sale, etc. of items and it can improve the performance of the system compared to the existing system.

Data mining, infrequent item set, utility item

[1].A Review on Infrequent Weighted Itemset Mining using Frequent Pattern Growth Shipra Khare , Prof. Vivek Jain, Shipra Khare et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 1642-1647, ISSN:0975-9646.
[2]. J.Jenifa, Dr.V. Sampath Kumar,” Study on Predicting Various Mining Techniques Using Weighted Item sets “, IOSR Journal of Pharmacy and Biological Sciences (IOSR-JPBS) e-ISSN: 2278-3008, p-ISSN:2319-7676. Volume 9, Issue 2 Ver. VI (Mar-Apr. 2014), PP 30-39.
[3]. Kamepalli,Sujatha and K. Raja Sekhara Rao,” a survey on infrequent pattern mining” International Journal of Advances in Engineering & Technology, ISSN: 22311963 ,1728 Vol. 6, Issue 4, pp. 1728-1732 ,Sept. 2013.
[4]. Sujatha Kamepalli1 , Raja Sekhara Rao Kurra and Sundara Krishna.Y.K, “ Apriori Based: Mining Infrequent and Non-Present Item Sets from Transactional Data Bases”, International Journal of Electrical & Computer Science IJECS-IJENS Vol:14 No:03.
[5] Ms. Kumudbala Saxena, Dr. C.S. Satsangi, “A Non Candidate Subset-Superset Dynamic Minimum Support Approach for sequential pattern Mining”, International Journal of Advanced Computer Research (IJACR),Volume-2 Number-4 Issue-6 December-2012.
[6] Dr. Manish Shrivastava, Mr. Kapil Sharma, MR. Angad Singh, “Web Log Mining using Improved Version of Proposed Algorithm”, International Journal of Advanced Computer Research (IJACR),Volume 1 Number 2 December 2011.
[7].Sakthi Nathiarasan, Kalaiyarasi, Manikandan, “Literature Review on Infrequent Itemset Mining Algorithms”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 8, August 2014 Copyright to IJARCCE www.ijarcce.com 7670 , ISSN (Online) : 2278-1021,ISSN (Print) : 2319-5940.
[8]. D.J. Haglin and A.M. Manning, “On Minimal Infrequent Itemset Mining,” Proc. Int’l Conf. Data Mining (DMIN ’07), pp. 141-147,2007.
[9].Gao Cong, Anthony K. H. Tung, Jiong Yang, “FARMER: Finding Interesting Rule Groups in Microarray Datasets
[10]. Luca Cagliero and Paolo Garza,” Infrequent Weighted Itemset Mining Using Frequent Pattern Growth”IEEE transactions on knowledge and data engineering, vol. 26, no. 4, april 2014.

Akilandeswari. S and A.V.Senthil Kumar, "A Novel Low Utility Based Infrequent Weighted Itemset Mining Approach Using Frequent Pattern," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.181-185, 2015.

Grid computing is a type of distributed computing system that provides access to various computational resources which are shared by different organizations, in order to create an integrated powerful virtual computer. Nowadays, grid is known as an essential technology which is used for different kinds of high performance applications and it is believed that it will be applied more and more in the future as technology progresses. Data replication is a common method used in distributed environments to improve ease of data access and to provide a high level of data availability, increased fault tolerance and data reliability; and that’s why this method is used for data management in data grid systems. Since the data files are very large and the Grid storages are limited, managing replicas in storage for the purpose of more effective utilization requires more attention. In this paper, a novel data replication strategy, called Replication with Dynamic Threshold (RDT) is proposed that uses a new threshold for characterizing the number of appropriate sites for replication. Appropriate sites have the higher number of access for that particular replica from other sites. It also minimizes access latency by selecting the best replica when various sites hold replicas. The simulated results with OptorSim, i.e. European Data Grid simulator show that the RDT strategy gives better performance compared to the other algorithms and prevents the unnecessary creation of replicas which leads to efficient storage usage.

Distributed systems; Data grid; Data replication; Dynamic Threshold; OptorSim

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Sheida Dayyani and Mohammad Reza Khayyambashi, "RDT: A New Data Replication Algorithm for Hierarchical Data Grid," International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.186-197, 2015.