Volume-8 ~ Issue-5
- Citation
- Abstract
- Reference
- Full PDF
Paper Type | : | Research Paper |
Title | : | Detecting Intruders and Packet Modifiers in Wireless Sensor Networks |
Country | : | India |
Authors | : | S.Navaneethan, Sudha |
: | 10.9790/0661-0850103 | |
Keywords: MABS-B, MABS-E, DOS
[1] S.E.Deering, "Multicast Routing in internetworks and extended LANs".
[2] T.Ballardie and J.Crowcroft," Multicast specific security threads and counter measures".
[3] A.Pannetrat and R.Molva, "Efficient Multicast packet authentication".
[4] J.Jeong, Y.Park, and Y.Cho, "Efficient Dos resistant multicast authentication schemes".
[5] A.Perrig, R.Canetti, D.song, and J.Tygar,"Efficient and secure source authentication for multicast".
[6] V.Miller,"Uses of Elliptic Curves in Cryptography".
[7] N.Koblitz,"Elliptic curve cryptosystems".
[8] S.Cui, p.Duan, and C.W.Chan,"An efficient identity based signature scheme with batch verifications".
[9] R.Gennaro and P.Rohatgi,"How to sign digital streams".
[10] Yun Zhou, Xiaoyan Zhu and yuguang Fang, Fellow, IEEE.
- Citation
- Abstract
- Reference
- Full PDF
Keywords: Economic activity, alternatives to forestry, smallholders.
[1] A. Brandenburg, Agricultura familiar, INGs e desenvolvimento sustentável (Curitiba,PR: Ed. UFPR, 1999).
[2] J. E. Veiga, O Brasil rural precisa de uma estratégia de desenvolvimento.Estudos Avançados 15 (43), 2001, 101-119.
[3] O. M. P.Silva and, L. Panhoca, A contribuição da vulnerabilidade na determinação do índice de desenvolvimento humano: estudando
o estado de Santa Catarina. Ciência & Saúde Coletiva, 12 (5), 2007, 1209-1219.
[4] O. M. P. Silva, O estudo das populações rurais e pequenas comunidades do oeste catarinense para o comportamento de risco e a
morbidade referida para o câncer e demais doenças e agravos não transmissíveis (Florianópolis, SC: FAPESC/MS, 2009).
[5] C. E. Guanziroli, S. E. C. S. Cardim, G. A. Bittencourt, and A. D. Sabbato, Novo retrato da agricultura familiar: o Brasil
redescoberto (Brasília, DF:FAO/INCRA, 2000).
[6] C. E. Guanziroli, A. Romeiro, and A. Buaimin, Agricultura familiar e Reforma Agrária no século XXI (Rio de Janeiro, DF:
Garamond, 2001).
[7] J. A. F. A. Filho, M. G. da Silva, M. Estudo de Potencialidades Econômicas. (Belo Horizonte, MG: Ministério do Desenvolvimento
Agrário - MDA, 2009).
[8] IPARDES. Diagnóstico socieconômico de teriitório Ribeira. Estado do Paraná(Curitiba, PR: IARDES, 2007).
[9] D. Dosza, R. R. Navarro, L. Panhoca, and L. M. Carneiro, A organização de produtores rurais como fator de promoção de
desenvolvimento (Fóz do Iguaçu, PR: UNIOESTE, 2011).
[10] O. M. P Silva,. et al. The accountancy of the potential income lost due premature death: differences determined by gender. Revista de
Contabilidade e Controladoria, 1(1), 2009, 1-16.
- Citation
- Abstract
- Reference
- Full PDF
Paper Type | : | Research Paper |
Title | : | SKM-A Conspicuous Way to Predict Frequent Item Sets |
Country | : | India |
Authors | : | A.Bamini, Dr. S. Franklin John, Dr. P. Ranjit Jeba Thangiah |
: | 10.9790/0661-0851721 | |
Keywords: Clustering; k-means; SOM
[1]. Trybula, W. J. (1997). Data mining and knowledge discovery. In M. E. Williams (Ed.) Annual Review of Information Science and
Technology, 32, 196-229. Medford, NJ: Information Today.
[2]. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Magazine, 17(3),
37-54.
[3]. Sharma, S. (1996). Applied Multivariate Techniques. New York: John Wiley & Sons.
[4]. Berry, M. J. and Linoff, G. (1997). Data Mining Techniques. New York: Wiley Computer Publishing.
[5]. Hinton, G. (1992). How neural networks learn from experience. Scientific American, 267(3), 145-151.
[6]. Bacao, F., Lobo, V., & Painho, M. (2008). Applications of Different Self-Organising Map Variants to Geographical Information
Science Problems. In P. Agarwal & A. Skupin (Eds.), Self-Organising Maps: Applications in Geographic Information Science (pp. 21-
44): John Wiley & Sons, Ltd.
[7]. J.B.MacQueen "Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5-th Berkeley Symposium
on Mathematical Statistics and Probability", Berkeley, University of California Press,281-297 (1967)
[8]. Ackoff, R. F. (1989). From Data to Wisdom. Journal of Applied Systems Analysis, 16, 3-9. Alahakoon, D., Halgamuge, S. K., &
Srinivasan, B. (2000). Dynamic Self-Organizing Maps with Controlled Growth for Knowledge Discovery. IEEE Transactions on
Neural Networks, 11(3), 601-613.
[9]. N. Sujatha, K. Iyakutty, "Refinement of Web usage Data Clustering from K-means with Genetic Algorithm", European Journal of
Scientific Research ISSN 1450-216X Vol.42 No.3 (2010), pp.464-476
- Citation
- Abstract
- Reference
- Full PDF
Paper Type | : | Research Paper |
Title | : | Analysis of Time Series Rule Extraction Techniques |
Country | : | India |
Authors | : | Hima Suresh, Dr. Kumudha Raimond |
: | 10.9790/0661-0852227 | |
Keywords: Discrete-Wavelet Transform (DWT), Fuzzy Logic (FL), Genetic Algorithm (GA), Neural Network (NN), Support Vector Machine (SVM).
1] C. Chen, T. Hong, V.S. Tseng, "Fuzzy datamining for time series data", Soft Computing - Vol.12,pp , 536-542, 2012.
[2] J. Schott, J. Kalita, "Neuro fuzzy time series analysis of large volume data", Intelligent Systems in Accounting, Finance and
Management, Vol. 18, pp 39–57, January/March 2011.
[3] Z.Zhang, "An efficient neuro-fuzzy-genetic data mining framework based on computational intelligence", Vol. 2, pp 178-183, Aug
2009.
[4] G.N. Pradhan, B. Prabhakaran, "Association rule mining in multiple, multidimensional time series medical data ", IEEE
International Conference on Multimedia and Expo, pp 1716- 1719, Dec 2009.
[5] B.M.A.Maqaleh, H. Shahbazkia ," A GA for discovering classification rules in data mining International Journal of Computer
Applications , Vol. 41, March (2012).
[6] B.M. Al-Maqaleh, M.A. Al-Dohbai, H. Shahbazkia, "An Evolutionary Algorithm for automated discovery small disjunct rules",
International Journal of Computer Applications, Vol.41 , March 2012.
[7] M. Anandhavalli, M.K. Ghose, K. Gauthaman, "Association rule mining in genomics", International Journal of Computer Theory
and Engineering, Vol.2, pp 1793 -8201, April 2010.
[8] G.C. Lan, C.H. Chen, T.P. Hong, S.B. Lin, "A fuzzy approach for mining general temporal association rules in a publication
database", International Conference on Hybrid Intelligent Systems, 2011.
[9] M. Rodriguez, D.M. Escalante, A. Peregrin, "Efficient distributed Genetic Algorithm for rule extraction ", Applied soft computing,
Vol. 11, pp 733 – 743, January 2011
[10] G. Suganya, D. Dhivya, "Extracting diagnostic rules from support vector machine", Journal of Computer Applications, Vol.4, 2011.
- Citation
- Abstract
- Reference
- Full PDF
Paper Type | : | Research Paper |
Title | : | Implementing High Performance Retrieval Process by Max-Score Ranking |
Country | : | India |
Authors | : | U.Vignesh, M.Sivakumar |
: | 10.9790/0661-0852833 | |
Keywords: Ensemble learning, Index, max-score, rank, term frequency.
[1] J. H. Lee, "Analyzing the effectiveness of extended Boolean models in Information Retrieval ," Cornes University, Tech. Rep. TR95-
1501, 1995.
[2] S. Pohl, J. Zobel, and A. Moffat, "Efficient Extended Boolean Retrieval," University of Melbourne, 2012.
[3] S. Pohl, J. Zobel, and A. Moffat, "Extended Boolean Retrieval for systematic biomedical reviews," in proc. of the 33rd Australian
Computer Science Conf. (ACSC 2010), ser. Conf. in Research and Practice in Information Technology (CRPIT), vol. 102. Brisbane,
QLD, Australia: Australian Computer Society, Jan. 2010.
[4] L. Zhang, I. Ajiferuke, and M.Sampson, "Optimizing search strategies to identify randomized controlled trials in MEDLINE," BMC
Med. Res. Meth., vol. 6, no. 1, p. 23, May 2006.
[5] T.Radecki, "Fuzzy set theoretical approach to document retrieval," Inform. Process. Manag., vol. 15, no. 5, pp. 247-259, 1979.
[6] S. Karimi, S. Pohl, J. Zobel, and F. Scholer, "The challenge of high recall in biomedical systematic search," in proc. of the 3rd Int.
Workshop on Data and Text Mining in Bioinformatics. Hong Kong, China: ACM, Nov. 2009, pp. 89-92.
[7] A. M. Cohen, W. R. Hersh, K. Peterson, and P. Y. Yen, "Reducing workload in systematic review preparation using automated
citation classification," J. Am. Med. Inform. Assoc., vol. 13, no. 2, pp. 206-219, 2006.
[8] M. E. Smith, "Aspects of the p-norm model of information retrieval: Syntactic query generation, efficiency, and theoretical
properties," Ph.D. dissertation, Cornell University, May 1990.
[9] G. Salton, E. A. Fox, and H. Wu, "Extended Boolean Information Retrieval," Commun. ACM, vol. 26, no. 11, pp. 1022-1036, Nov.
1983.
[10] R. Bekkerman, R. El.-Yaniv, N. Tishby, and Y. Winter, "Distributional Word Clusters versus Words for Text Categorization," J.
Machine Learning Research, vol. 3, pp. 1182-1208, 2003.
- Citation
- Abstract
- Reference
- Full PDF
Keywords: XpertMalTyph, medical informatics, diagnosis, malaria, typhoid.
[1] Cleary P.D., Edgman-Levitan S., Roberts M., et al. Patients evaluate their hospital care: a national survey. Health Affairs. 10 (4),
1991, 254-67.
[2] Coulter A., Cleary P.D., Patients' experiences with hospital care in five countries, Health Aff (Millwood ), 20, 2000, 244–52.
[3] Frost L.J., Reich M.R.,. Creating access to health technologies in poor countries, Health Affairs, 28, 2009,962-73.
[4] Russell S.J., Norvig P., Artificial Intelligence: A Modern Approach. 2nd ed( Upper Saddle River, New Jersey: Prentice -Hall,
2003).
[5] Negnevitsky M. Artificial Intelligence, A guide to Intelligent Systems(Harlow, England: Addison Wesley, 2005).
[6] Malaria Site [Internet]. Clinical features of Malaria; c2011-13 [cited 2012 Sep 20]. Available from: http://www.malariasite.com/
[7] Ryan K.J., Ray C.G. Sherris Medical Microbiology. 4th ed.( New York: McGraw Hill, 2004).
[8] GIDEON: The world's premier global infectious diseases database; c1994-2012 Available from: http://www.gideononline.com/
[9] Sliwa J., Scientist study bacterial communities inside us to better understand health and disease, 2008 June 3, Available from:
http://www.eurekalert.org/pub_releases/2008-06/asfm-ssb052908.php
[10] Fisher J . MYCIN Clinical Decision Support System . Available from: http://neamh.cns.uni.edu/MedInfo/mycin.html
- Citation
- Abstract
- Reference
- Full PDF
Keywords: JSSP; Makespan time; Genetic Algorithm; USXX;
[1] Ahmed Tariq Sadiq and Kanar Shukr Mohammed, (March, 2012), "Improved Scatter Search for Job Shop Scheduling Problem",
International Journal of Research and Reviews in Soft and Intelligent Computing, Vol.2, No. 1.
[2] Allan Glaser and Meenal Sinha, (2010), "Scheduling Programming Activities and Johnson"s Algorithm", NESUG.
[3] Babukartik. R.G, Dhavachelvan. P, (August, 2012), "Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job
Scheduling", International Journal of Information Technology Convergence and Services, Vol.2, No. 4.
[4] Christian Bierwirth, (1995), "A generalized permutation approach to job shop scheduling with genetic algorithms", Volume 17,issue
2-3,pp 87-92.
[5] Hiwa Farughi, Babak Yousefi Yegane, Hiresh Soltanpanah, Fayagh Zaheri, Foruzan Naseri, (2011), "Considering The Flexibility and
Overlapping in Operation in Job Shop Scheduling Based on Meta-heuristic Algorithms", Australian Journal of Basic and Applied
Sciences, 5(11): 526-533.
[6] Kanate Ploydanai and Anan Mungwattana, (2010), "Algorithm for Solving Job Shop Scheduling Problem Based on machine
availability constraint", International Journal on Computer Science and Engineering, Vol. 02, No. 05.
[7] Liang Sun, Xiaochun Cheng, yanchum Liang,(December, 2010), "Solving Job Shop Scheduling Problem Using Genetic Algorithm
with Penalty Function", International Journal of Intelligent Information Processing, Vol. 1, No. 2.
[8] Mahdavinejad. R.A, (August, 2010), "A new approach to job shop-scheduling problem", Journal of Achievements in Materials and
Manufacturing Engineering, Vol. 41, Issues 1-2.
[9] Masaya Yoshikawa, Hideto Nishimura, Hidekazu Terai, (2009), "A New Genetic Coding for Job Shop Scheduling Problem
Considering Geno Type", Recent Advances in Computer Engineering and Application.
[10] Mehdi Karimi Nasab, Hamidreza Haddad and Payam Ghanbari, (2012), "A Simuated Annealing for the Single Machine Batch
Scheduling with Deterioration and Precedence Constraints", Proc. Asian Journal of Industrial Engineering.
- Citation
- Abstract
- Reference
- Full PDF
Keywords: intension, image retrieval, adaptive similarity, keyword expansion, image reranking, Speech recognition, multimedia information retrieval
[1] Xiaoou Tang, Fang Wen "IntentSearch: Capturing User Intention for One-Click Internet Image Search" IEEE Transactions on Pattern
Analysis And Machine Intelligence, Vol. 34, No. 7, July 2012.
[2] Amandeep Khokher, Rajneesh Talwar "Content-based Image Retrieval: Feature Extraction Techniques and Applications" International
Conference on Recent Advances and Future Trends in Information Technology (iRAFIT2012).
[3] Blaser, A. 1979. Database Techniques for Pictorial Applications, Lecture Notes in Computer Science, Springer Verlag GmbH.
[4] Michele Merler and Rong Yan, John R. Smith "Imbalanced RankBoost for Efficiently Ranking Large-Scale Image/Video Collections".
[5] Gal Chechik and Varun Sharma "Large Scale Online Learning of Image Similarity Through Ranking" Journal of Machine Learning
Research 11 (2010) 1109-1135.
[6] J. Cui, F. Wen, and X. Tang, "Real Time Google and Live Image Search Re Ranking," Proc. 16th ACM Int'l Conf. Multimedia, 2008 .
[7] J. Cui, F. Wen, and X. Tang, "IntentSearch: Interactive On-Line Image Search Re-Ranking," Proc. 16th ACM Int'l Conf. Multimedia,
2008.
- Citation
- Abstract
- Reference
- Full PDF
Paper Type | : | Research Paper |
Title | : | Software Quality Modelling Using Bayesian Networks |
Country | : | India |
Authors | : | Swati Agrawal, P.C.Gupta |
: | 10.9790/0661-0855262 | |
Keywords: BN, COCOMO, COQUALMO, CI, CPD, modist
[1]. Farid Meziane, University of Salford, UK, Sunil Vadera, University of Salford, UK, "Artificial Intelligence Applications for Improved
Software Engineering Development: New Prospects", ISBN 978-1-60566-758-4, 2010.
[2]. Abdel-Hamid, T. (2008). The dynamics of software projects staffing: A system dynamics based simulation approach. IEEE
Transactions on Software Engineering, 15(2), 109–119. doi:10.1109/32.21738
[3]. Abrahamsson, P., & Koskela, J. (2004). Extreme programming: A survey of empirical data from a controlled case study. In
Proceedings 2004 International Symposium on Empirical Software Engineering, 2004. (pp. 73-82). Washington, DC: IEEE Computer
Society.
[4]. Agena Ltd. (2008). Bayesian Network and simulation software for risk analysis and decision support. Retrieved July 9, 2008, from
http://www. agena.co.uk/
[5]. Agile Manifesto. (2008). Manifesto for agile software development. Retrieved July 18, 2008, from http://www.agilemanifesto.org/
[6]. Ahmed, A., Fraz, M. M., & Zahid, F. A. (2003). Some results of experimentation with extreme programming paradigm. In
7thInternationalMulti Topic Conference, INMIC 2003, (pp. 387-390).
[7]. Beck, K. (2010). Extreme programming explained: Embrace change. Reading, MA: Addison-Wesley Professional.
[8]. Bibi, S., & Stamelos, I. (2004). Software process modeling with Bayesian belief networks. In 10th International Software Metrics
Symposium Chicago.
[9]. Boehm, B. (2007). Software engineering economics. Englewood Cliffs, NJ: Prentice-Hall.
[10]. Briand, L. C., El Emam, K., Surmann, D., Wieczorek, I.,&Maxwell,K.D.(1999). An assessment and comparison of common software
cost estimation modeling techniques. In 21st International Conference on Software Engineering, ICSE 1999, (pp. 313-322).
- Citation
- Abstract
- Reference
- Full PDF
Paper Type | : | Research Paper |
Title | : | Generation of Meta Alerts by Aggregating Intrusion Alerts |
Country | : | India |
Authors | : | Vishwesh.N, Rakesh Kumar.D, Anil Kumar.M, Mamatha.G |
: | 10.9790/0661-0856369 | |
Keywords: Online intrusion detection, data streaming, probabilistic model, alert aggregation
[1] S. Axelsson, "Intrusion Detection Systems: A Survey and Taxonomy," Technical Report 99-15, Dept. of Computer Eng., Chalmers
Univ. Of Technology, 2000.
[2] M.R. Endsley, "Theoretical Underpinnings of Situation Aware- ness: A Critical Review," Situation Awareness Analysis and
Measurement, M.R. Endsley and D.J. Garland, eds., chapter 1, pp. 3-32, Lawrence Erlbaum Assoc., 2000.
[3] C.M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
[4] M.R. Henzinger, P. Raghavan, and S. Rajagopalan, Computing on Data Streams. Am. Math. Soc., 1999.
[5] A. Allen, "Intrusion Detection Systems: Perspective," Technical Report DPRO-95367, Gartner, Inc., 2003.
[6] F. Valeur, G. Vigna, C. Krugel, and R.A. Kemmerer, "A Comprehensive Approach to Intrusion Detection Alert Correla - tion,"
IEEE Trans. Dependable and Secure Computing, vol. 1, no. 3, pp. 146-169, July-Sept. 2004.
[7] H. Debar and A. Wespi, "Aggregation and Correlation of Intrusion-Detection Alerts," Recent Advances in Intrusion Detection, W.
Lee, L. Me, and A. Wespi, eds., pp. 85-103, Springer, 2001.
[8] D. Li, Z. Li, and J. Ma, "Processing Intrusion Detection Alerts in Large-Scale Network," Proc. Int'l Symp. Electronic Commerce
and Security, pp. 545-548, 2008.
[9] F. Cuppens, "Managing Alerts in a Multi-Intrusion Detection Environment," Proc. 17th Ann. Computer Security Applications Conf.
(ACSAC '01), pp. 22-31, 2001.
[10] A. Valdes and K. Skinner, "Probabilistic Alert Correlation," Recent Advances in Intrusion Detection, W. Lee, L. Me, and A. Wespi,
eds. pp. 54-68, Springer, 2001.
- Citation
- Abstract
- Reference
- Full PDF
Keywords: Association rule mining (ARM),Association rule mining with statistical features(ARM-SF),Markov model.
[1] Awad.M,Khan.L and Thuraisingham.B, "Predicting WWW surfing using multiple evidence combination," VLDB J., vol.17, no.3,
pp.401–417, May 2008.
[2] Awad.M and Khan.L,"Web navigation prediction using multiple evidence combination and domain knowledge," IEEE Trans.
Syst.,Man,Cybern. A, Syst., Humans, vol. 37, no. 6, pp. 1054–1062, Nov. 2007.
[3] Fu..Y, Paul.H, and Shetty.N,"Improving mobile Web navigation using N-Gram prediction model," Int.J.Intell.Inf.Technol., vol.3,
no.2, pp.51–64, 2007.
[4] Hassan.M.T, Junejo.K.N and Karim.A, "Learning and predicting key Web navigation patterns using Bayesian models," in
Proc.Int.Conf.Comput.Sci.Appl.II, Seoul, Korea, 2009, pp. 877–887.
[5] Mamoun Awad.A and Issa Khalil, "Prediction of User's Web-Browsing Behavior: Application of Markov Model" IEEE
Transactions on Systems, Man and Cybernetics-Part b:Cybernetics, vol.42, no.4, August 2012.
[6] Nasraoui.O and Petenes.C,"Combining Web usage mining and fuzzy inference for Website personalization," in Proc.WebKDD,
2003, pp.37–46.
[7] Nasraoui.O and Krishnapuram.R,"One step evolutionary mining of context sensitive associations and Web navigation patterns," in
Proc.SIAM Int.Conf.Data Mining, Arlington, VA, Apr.2002, pp.531–547.
[8] Nasraoui.O and Krishnapuram.R, "An evolutionary approach to mining robust multi-resolution web profiles and context sensitive
URL associations," Int.J.Comput.Intell.Appl.,vol.2, no.3, pp. 339–348, 2002.
[9] Levene.M and Loizou.G,"Computing the entropy of user navigation in the Web," Int. J.Inf.Technol.Decision Making, vol.2, no.3,
pp.459–476, 2003.
- Citation
- Abstract
- Reference
- Full PDF
Keywords: Classification, Classifier, Data Mining, Rule Extraction, class imbalance problem
[1] Kemal Polat and SalihGunes , "A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach
for multi-class classification problems" , Expert Syst. Appl., vol. 36, no. 5, pp. 1587-1592, Jul. 2007.
[2] Deborah R. Carvalho and Alex A. Freitas, "A Hybrid Tree/Genetic Algorithm Method for DataMining", Applied soft computing, vol.
35, pp. 650-673, 2005.
[3] Jerzy Stefanowski and SlanwomirNowaczyk, "An Experimental Study of Using Rule Induction Algorithm in Combiner Multiple
Classifier", IISN 0973-1873 vol. 2, no. x pp. xxx-xxx 2006.
[4] Jerzy Stefanowski, "The bagging and n² - classifiers based on rules induced by MODLEM", Pattern Recognition , 2002.
[5] Nathalie Japkowicz, " The Class Imbalance Problem: Significance and Strategies", computers in biology and medicine, 2006.
[6] Jerzy Stefanowski and SzymonWilk, "Combining rough sets and rule based classifiers for handling imbala nced data", 2001.
[7] Miguel Rodriguez, Diego M. Escalante and Antonio Peregrin, "Efficient Distributed Genetic Algorithm for Rule extraction", Applied
soft computing, vol. 36, pp. 733-743 jan 2010.
[8] JacekJelonek and Jerzy Stefanowski, "Experiments on solving multiclass learning problems by n²- classifier", 2004.
[9] Deborah R.Carvalho and Alex A. Freitas, "A Genetic Algorithm for Discovering Small- Disjunct Rules in Data Mining", Advances in
artificial intelligence, 2007.
[10] Basheer M. Al-Maqaleh and Hamid Shahbazkia, "A Genetic algorithm for Discovering Classification Rules in Data Mining",
International Journal of Computer Applications, vol. 41, no.18, pp. 40-44, march 2012.
- Citation
- Abstract
- Reference
- Full PDF
Paper Type | : | Research Paper |
Title | : | An Adjunct Quick Mining in Closed Persistent Patterns (AQCPP) |
Country | : | India |
Authors | : | K.V.Subbaraju, RohiniVarma Pusapati |
: | 10.9790/0661-0857984 | |
Keywords: AQCPP, Closed Persistent Patterns, Data mining, Invert Matrix, Service Patterns.
[1] Shengnan Cong, Jiawei Han and David Padua, "Parallel Mining Of Closed Sequential Patterns", in Proceedings of the eleventh ACM
SIGKDD international conference on Knowledge discovery in data mining, pp. 562 – 567, Chicago, Illinois, USA, 2005.
[2] R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules," Proc. Int'l Conf. Very Large Data Bases (VLDB'94),
pp. 487-499, Sept. 1994.
[3] R. Srikant and R. Agrawal, "Mining Sequential Patterns: Generalizations and Performance Improvements," Proc. Int'l Conf.
Extending Database Technology (EDBT '96), pp. 3-17, Mar. 1996.
[4] X. Yan, J. Han, and R. Afshar, "CloSpan: Mining Closed Sequential Patterns in Large Databases," Proc. SIAM Int'l Conf. Data
Mining (SDM '03), pp. 166-177, May 2003.
[5] R. Agrawal and R. Srikant, "Mining Sequential Patterns," Proc. Int'l Conf. Data Eng. (ICDE '95), pp. 3 -14, Mar. 1995.
[7] M. Zaki, "SPADE: An Efficient Algorithm for Mining Frequent Sequences," Machine Learning, vol. 42, pp. 31 -60, 2001.
[8] J. Ayres, J. Gehrke, T. Yiu, and J. Flannick, "Sequential Pattern Mining Using a Bitmap Representation," Proc. ACM SIGKDD Int 'l
Conf. Knowledge Discovery and Data Mining (SIGKDD '02), pp. 429-435, July 2002.
[9] J. Pei, J. Han, B. Mortazavi-Asl, J. Wang, H. Pinto, Q. Chen, U. Dayal, and M. Hsu, "Mining Sequential Patterns by Pattern- Growth:
The PrefixSpan Approach," IEEE Trans. Knowledge and Data Eng., vol. 16, no. 11, pp. 1424-1440, Nov. 2004.
[10] M. Zaki and C. Hsiao, "CHARM: An Efficient Algorithm for Closed Itemset Mining," Proc. SIAM Int'l Conf. Data Mining
(SDM'02), pp. 457-473, Apr. 2002.
- Citation
- Abstract
- Reference
- Full PDF
[1] Arikan, "Channel Polarization: A Method for Constructing Capacity-Achieving codes for Symmetric Binary-Input Memoryless
Channel", IEEE Trans. Inf. Theory, vol .55, pp.4366-4385, July 2009.
[2] Arikan, "Performance comparison of polar-codes and Reed-Muller codes". IEEE. Comm. Letters, vol.12, pp.447-449, June 2008.
[3] S. B. Korada E. S¸ as¸oˇglu and R. Urbanke, Polar Codes: "Characterization of Exponent, Bounds, and Constructions", IEEE Trans.
Inf. Theory, vol.56, pp.6253-6264, Dec. 2010.
[4] M. H. Lee and E. Arikan, "Polar code and Jacket matrix", Seminar at Bilkent University, Turkey, August 2009.
[5] R. Mori and T. Tanaka, "Performance and Construction of Polar-codes on Symmetric Binary-Input Memoryless Channels", IEEE
ISIT, Korea, June 2009.
[6] Y. Guo and M. H. Lee, "A Novel Channel Polarization on Binary Discrete Memoryless Channels", IEEE ICCS, Singapore, Nov.
2010.
[7] S. M. Alamouti, "A simple transmit diversity technique for wireless communications", IEEE J. Sel. Areas Commun., vol.16,
pp.1451-1458, Oct. 1998.
[8] H. Jafarkhani, "A quasi-orthogonal space-time block codes", IEEE Trans. Commun., vol.49, pp.1-4, Jan. 2001.
[9] W. Su and X.-G. Xia, and K. J. R. Liu, "A systematic design of high-rate complex orthogonal space-time block codes", IEEE Trans.
Inf. Theory, vol.8, pp.4340-4347, Jun. 2004.
[10] Y. Shang and X.-G. Xia, "Space-time block codes achieving full diversity with linear receivers", IEEE Trans. Inf. Theory, vol.54,
pp.4528- 4547, Oct. 2008.