Digital communication has become an essential part of infrastructure now-a-days, a lot of applications are Internetbased and in some cases it is desired that the communication be made secret. Two techniques are available to achieve this goal: cryptography and steganography. In this paper, various digital steganographic techniques are implemented which are capable of producing a secret-embedded image that is indistinguishable from the original image to the human eye. A comparative analysis is made to demonstrate the effectiveness of the proposed methods. The effectiveness of the proposed methods has been estimated by computing Mean square error (MSE) and Peak Signal to Noise Ratio (PSNR).
Abstract-Association rule min ing aims to determine the relations among sets of items in transaction database and data repositories. It generates informative patterns fro m large databases. Apriori algorithm is a very popular algorith m in data min ing for defining the relationships among itemsets. It generates 1, 2, 3,…, n-item candidate sets. Besides, it performs many scans on transactions to find the frequencies of itemsets which determine 1, 2, 3,…, n-item frequent sets. This paper aims to erad icate the generation of candidate itemsets so as to minimize the processing time, memo ry and the number of scans on the database. Since only those itemsets which occur in a transaction play a vital ro le in determining frequent itemset, the methodology that is proposed in this paper is extracting only single itemsets fro m each transaction, then 2,3,..., n itemsets are generated from them and their corresponding frequencies are also calculated. Further, each transaction is scanned only once and no candidate itemsets is generated both resulting in minimizing the memo ry space for storing the scanned itemsets and minimizing the processing time too. Based on the generated itemsets, association ru les are generated using minimum support and confidence.
Association rule mining is a data mining technique which is used to identify decision-making patterns by analyzing datasets. Many association rule mining techniques exist to find various relationships among itemsets. The techniques proposed in the literature are processed using non-distributed platform in which the entire dataset is sustained till all transactions are processed and the transactions are scanned sequentially. They require more space and are time consuming techniques when large amounts of data are considered. An efficient technique is needed to find association rules from big data set to minimize the space as well as time. Thus, this paper aims to enhance the efficiency of association rule mining of big transaction database both in terms of memory and speed by processing the big transaction database as distributed file system in MapReduce framework. The proposed method organizes the transactions into clusters and the clusters are distributed among many parallel processors in a distributed platform. This distribution makes the clusters to be processed simultaneously to find itemsets which enhances the performance both in memory and speed. Then, frequent itemsets are discovered using minimum support threshold. Associations are generated from frequent itemsets and finally interesting rules are found using minimum confidence threshold. The efficiency of the proposed method is enhanced in a noticeably higher level both in terms of memory and speed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.