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Pavithra, A.
- Reduction of Test Data Volume Based On Viterbi Compression Algorithm
Abstract Views :28 |
PDF Views:3
Authors
Affiliations
1 P. A. College of Engineering and Technology, Pollachi-2, IN
1 P. A. College of Engineering and Technology, Pollachi-2, IN
Source
Software Engineering, Vol 7, No 3 (2015), Pagination: 74-79Abstract
Test vector compression has been an active area of research, yielding a wide variety of techniques. A test pattern compression scheme is proposed in order to reduce test data volume and application time. The proposed scheme finds a set of compressed test vectors using the Viterbi algorithm instead of solving linear equations. By assigning a cost function to the branch metric of the Viterbi algorithm, an optimal compressed vector is selected among the possible solution set. This feature enables high flexibility to combine various test requirements such as low-power compression and/or improving capability to repeat test patterns. The proposed on chip decompressor follows the structure of Viterbi encoders which require only one input channel. Experimental results compared with the dictionary algorithm compression. Dictionary based compression techniques are also popular in embedded systems domain. Since they provide a dual advantage of good compression efficiency as well as fast decompression mechanism. Viterbi algorithm provides better compression ratio compared to dictionary algorithm.Keywords
Test Data Compression, Viterbi, Dictionary, Compression Efficiency.
- Comparative Study of Effective Performance of Association Rule Mining in Different Databases
Abstract Views :29 |
PDF Views:4
Authors
A. Pavithra
1,
S. Dhanaraj
2
Affiliations
1 Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, IN
2 Department of Information Technology, Sree Saraswathi Thyagaraja College, Pollachi, IN
1 Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi, IN
2 Department of Information Technology, Sree Saraswathi Thyagaraja College, Pollachi, IN
Source
Data Mining and Knowledge Engineering, Vol 10, No 4 (2018), Pagination: 74-77Abstract
Data mining practices expert procedures and methods to identify the tendencies and profiles concealed in data. Mining is an iterative process in a sequence. Different sources of data are stored in different databases. The mining depends on databases. This research is for various association rule mining applications of different databases. There are different databases in practice like large database, distributed database, medical database, relational database, spatial database. The process of mining these databases are carried out by different data mining techniques. For making decisions, association rule is most essential. They are associated with association rule mining techniques.
Keywords
Data Mining, Association Rule Mining, Spatial Data Mining, RDBMS, Medical Database, Large Database, Distributed Database.References
- J. Arokia Renjit and Dr.K.L.Shunmuganathan,” Mining the Data from Distributed Database Using an Improved Mining Algorithm”, Vol. 7, No. 3, March 2010 [(IJCSIS) International Journal of Computer Science and Information Security].
- Brijs, T., Goethals, B., Swinnen, G., Vanhoof, K. and Wets. G, “A Data Mining Framework for Optimal Product Selection in Retail Supermarket Data: The Generalized PROFSET Model”, 2000 [SIGKDD].
- Brijs, T., Swinnen, G., Vanhoof, K. and Wets.G, “Using Association Rules for Product Assortment Decisions: A Case Study”, 1999 [SIGKDD].
- Dong, J., Perrizo, W., Ding, Q. and Zhou, J., “The application of association rule mining to remotely sensed data”, 2000 [Proceedings of the ACM symposium on applied computing].
- Lee, W., Stolfo, S.J. and Mok, K.W., “A data mining framework for building intrusion detection models”, 1999 [IEEE Symposium on Security and Privacy].
- M Deypir and M H Sadreddini, “Distributed Association Rules Mining Using Non- derivable Frequent Patterns”, Vol. 33, Issue B6, pp. 511-26, 2009 [Iranian Journal of Science & Technology, Transaction B: Engineering].
- Maurice Houtsma and Arun Swami,” Set-oriented mining for association rules in relational databases”, Vol. 17, pp. 245-262, 1995 [Data and Knowledge Engineering].
- Pallavi Dubey, “Association Rule Mining on Distributed Data”, Vol. 3, Issue 1, ISSN: 2229-5518, 2012 [International Journal of Scientific & Engineering Research].
- Rakesh Agrawal, Tomasz Imielinski and Arun Swami, “Mining Association Rules between Sets of Items in Large Databases “, May 1993 [Proceedings of the ACM SIGMOD Conference, Washington DC, USA].
- Satou, K., Shibayama, G., Ono, T., Yamamura, Y., Furuichi, E., Kuhara, S. and Takagi, T., “Finding association rules on heterogeneous genome data”, 1997 [PSB].
- Tyler H. McCormick, C Rudin and D Madigan, “A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction”, 2000 [Submitted to the Annals of Applied Statistics].
- Wang, K. and Su, M.Y., “Item Selection by Hub-Authority Profit Ranking”, 2002 [SIGKDD].
- Xinhang Xu, Qiuhong S, Hongtao Z, Lei W and Y Liu, “Study and Application on the Method of Association Rules Mining Based on Genetic Algorithm”, 2012 [The 2nd International Conference on Computer Application and System Modeling].