Volume-07 , Special Issue-02 , Jan - 2019 Go Back

Nowadays, the whole world works in modern technology. Peoples are paying more attention to complete the process in a sophisticated way without putting the much human effort. End-user thinks that only poor knowledge about application usage leads to data security issues. But application providing companies camouflage the truth behind the name of the user sophistication. Even a small piece of a script code can make the huge data security issues. That type of problems is called cryptocurrency malware. Cryptomining malware is nothing but software code and designed to take system resources without the knowledge of authorized end users. The role of cryptomining is stealing computer’s resources with the help of auto inject of malware code in a crook way while making online communications on websites. A website owner uses these malware codes to take visitor`s system utilization to gain more earning. Cryptomining malware easily injects electronic devices like computers, smart phones to increase earnings from cryptocurrency mining.

Component, Formatting, Style, Styling, Insert (key words)

[1] https://malwaretips.com/blogs/remove-coinhive-miner-virus
[2] https://www.getastra.com/blog/911/remove-crypto-mining-malware-cms-wordpress-magento-drupal/
[3] https://www.cyber.nj.gov/threat-profiles/exploit-kit-variants/rig
[4] https://en.wikipedia.org/wiki/Fireball_(software)
[5] https://ethereumworldnews.com/researcher-finds-nearly-50000-websites-running-cryptocurrency-mining-malware
[6] https://www.theregister.co.uk/2018/02/11/browsealoud_compromised_coinhive

K.Berlin, S.S. Dhenakaran, "Impact of Crypto-Mining Malware on System Resource Utilization", International Journal of Computer Sciences and Engineering, Vol.07, Issue.02, pp.1-4, 2019.

In health care industries many concerns preserve their medical data as electronic data. In India especially for Tamil Nadu people are affected by H1N1 Swine flu is the name for the influenza type a virus that affects pigs (swine). This paper recognizes the various risk elements associated with high level of infection and can include fever, bronchitis, and then using the data mining clustering algorithms to find novelty hidden pattern which helpful to take decision and construction of societal monetary real world health hazard. In Present there is a necessary for accomplished data mining analysis tools to decide and making patters for the health hazards. To recognize the interesting patterns and knowledge based intellectual methods are applied to extract data patterns and scrutinize the worth of a diversity of data mining techniques are used in this health domain.

Data Mining, H1N1 Swine flu, Clustering, Pattern Evaluation, Health Hazards

[1].https://timesofindia.indiatimes.com/topic/Swine-Flu ssFever, cough, running nose, sore throat.
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[5].Data mining Learning models and Algorithms for medical applications– Whitepaper - PlamenaAndreeva, Maya Dimibova, PetraRadeve.
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V. Kayalvizhy, M. IdaRose, "Pattern Analysis of H1N1 Swine flu using Data Mining Technique in Health Hazards", International Journal of Computer Sciences and Engineering, Vol.07, Issue.02, pp.5-8, 2019.

Now-a-days, increasing volume of digital data demands more storage space and efficient technique to handle these data. Duplicate is inevitable while handling huge amount of data. Data Deduplication is an efficient approach in storage environment that utilize different techniques to deal with duplicate data. It identifies and removes redundant data by its message digest value which is generated by using any kind of cryptographic hash algorithms such as MD5 and Secure hash algorithms. This research paper analyses the two hash algorithms, namely MD5 and SHA, based on different parameters and compare the time taken to build a hash value.

Data Deduplication, Chunking, Hash Function, MD5, SHA-1, SHA-2

[1] Jean-Sebastien Coron, Yevgeniy Dodis, Cecile Malinaud, and Prashant Puniya, Merkle-Damgard Revisited : how to Construct a Hash Function. CDMP05.pdf.
[2] Wikipedia,http://en.wikipedia.org/wiki / Merkle-Damg% C3% A 5rd _has hash function.
[3] Rivest, R. The MD5 Message-Digest Algorithm", RFC 1321, April 1992.
[4] Aiden Bruen, David Wehlau, Mario Forci --nito, Hash Functions Based on Sylvester Matrices," Patents Oce Kilkenny, September 20th 2001.
[5] D. R. Stinson, Some observations on the theory of cryptographic hash func-tions, University of Waterloo, Canada, March 2, 2001.
[6] Rudiger Weis, Stefan Lucks, Cryptogra-phic Hash Functions, Recent Results on Cryptanalysis and their Implica-tions on System Security," Univeristy of Manheim.
[7] Xiaoyun Wang, Hongbo Yu, How to Break MD5 and Other Hash Functions, Shandong University, Jinan 250100, China.

J. Maria Selvam, P. Srivaramangai, "Comparative Analysis of Hashing Algorithms used in Data Deduplication", International Journal of Computer Sciences and Engineering, Vol.07, Issue.02, pp.9-13, 2019.

Mobile Ad hoc NETworks (MANETs) are dynamic networks, which is populated by mobile stations. Stations in MANETs includes laptops, PDAs or mobile phones. Current types of Wireless Networks are Cellular Networks, Mobile Ad Hoc Network, Wireless Sensor Networks, Vehicle Ad-hoc Networks, and Wireless Mesh Networks. Due to advance in technologies made possibility tremendous usage of small-size and high-performance computing and communication devices like commercial laptops and personal digital assistants. Success of second generation mobile system made more interest in wireless communication. Which led more interest to two types of wireless networks: infrastructured wireless network and infrastructureless wireless network, which is also called Mobile Ad-Hoc Network (MANET). The Infrastructureless wireless network consists of a network with mobile nodes with fixed wired base stations. Simulation tools are used by researchers to debug and test the reliability and Quality of Service of network protocols and also for hardware equipment. This made simulation a very prominent step towards the deployment of wireless communication networks. Hence this paper is very useful to researchers and engineers to propose and bring out new routing protocols using simulator in Mobile Ad Hoc Networks.

MANET, Routing, Mobility Models, Simulators, NS2

[1] S. Mehta, N. Ullah, M.H. Kabir, M. N. Sultana and Kyung Sup Kwak, “A Case Study of Networks Simulation Tools for Wireless Networks,” IEEE 3rd Asia International Conference on Modelling & Simulation; Bali, pp. 661-666, May 2009.
[2]Network simulation [Online]. Available: http://en.wikipedia.org/wiki/Network_simulation
[3] Jianli Pan, Raj Jain, A Survey of Network Simulation Tools: Current Status and Future Developments, Project report.
[4] NS3 Official Website: [Online]. Available : http://www.nsnam.org
[5] George F. Riley and Thomas R. Henderson, “The ns-3 Network Simulator,” Modeling and Tools for Network Simulation, Springer, pp 15-34, 2010.
[6] Elias Weingartner, Hendrik vom Lehn and Klaus Wehrle, “A performance comparison of recent network simulators,” IEEE International Conference on Communications; Dresden, pp.1-5, 14-18 June 2009.
[7] P. Pablo Garrido, Manuel P. Malumbres and Carlos T. Calafate, “ns-2 vs. OPNET: a comparative study of the IEEE 802.11e technology on MANET environments,” ACM 1st International conference on Simulation tools and techniques for communications, networks and systems & workshops, Belgium, 2008.
[8] OMNET++ discrete event simulator. [Online]. Available: http://www.omnetpp.org
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[11] András Varga and Rudolf Hornig “An Overview of The OMNet++ Simulation Environment,” IEEE 1st International conference, on Simulation tools and techniques for communications, networks and systems & workshops,pp. 1-10, 2008.
[12]NetSim Wekipedia: [Online]. Available: http://en.wikipedia.org/wiki/NetSim
[13] NetSim official: [Online]. Available: http://www.tetcos.com
[14]OPNET [Online] Availabe: http://www.opnet.com/university_program/teaching_with_opnet/textbooks_and_materials/index.html
[15] OPNET Wikipedia [Online]. Available: http://en.wikipedia.org/wiki/OPNET,
[16] Shabana Razak, Mian Zhou and Sheau-Dong Lang, “Network Intrusion Simulation Using OPNET,” In OPNETWORK Conference. pp. 1-5, September 2002.
[17]REAL:[Online].Available: http://www.cs.cornell.edu/skeshav/real/overview.html
[18] Jaroslav Kacer, J-Sim – A Java-based Tool for Discrete Simulations, Technical Report No. DCSE/TR-2001-05, September, 2001.
[19]J-Sim official site: [Online]. Available: https://sites.google.com/site/jsimofficial/
[20] C.S.R. Murthy and B.S.Manoj, “Ad hoc Wireless Networks: Architectures and Protocols”, Prentice Hall, 2004.
[21] Carlos de Morais Cordeiro and Dharma Prakash Agrawl,” Ad Hoc and Sensor Networks, Theory and Applications,2nd edition, Cambridge university press India press Pvt..Ltd, 2013.
[22] Sunil Kumar S.Manvi and Mahabaleshar S.Kakkasageri, “Wireless and Mobile Networks Concepts and Protocols”,Jon Willy and Sons publication, 2013.
[23] M. Bheemalingaiah1, C. Venkataiah2, K. Vinay Kumar3, M. M. Naidu4, D. Sreenivasa Rao, “ Survey of Energy Aware On-demand Multipath Routing Protocols in Mobile Ad Hoc Networks”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6, Issue 4, April 2016.
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[25] Xiaoyan Hong, Kaixin Xu, and Mario Gerla, “Scalable Routing Protocols for Mobile Ad Hoc Networks”, Journal of IEEE Network, July/August, 2002.
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[28] The Georgia Tech Network Simulator (GTNetS) , http://www.ece.gatech.edu/research /labs/MANIACS/GTNetS
[29] M Saba Siraj, Ajay Kumar Gupta, and Rinku-Badgujar “Network Simulation Tools Survey”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 1, Issue 4, PP: 201-210, June 2012
[30] J. Broch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, and J. Jetcheva,” A performance comparison of multi-hop wireless ad hoc network routing protocols”, in Proceedings of the Fourth Annual ACM/IEEE International Conference onMobile Computing and Networking (Mobicom98), ACM, October 1998.
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[40] Hayder Majid Abdulhameed Alash thesis on “Impact of Mobility Models On Routing Protocols for Various Traffic Classes in Mobile Ad Hoc Networks”, Kent State University, 2016, Investigation of Network Simulation Tools and Comparison Study: NS3 vs NS2

S. Menaka, S. Duraisamy, "An overview of Mobile Ad-hoc Simulation tools", International Journal of Computer Sciences and Engineering, Vol.07, Issue.02, pp.14-18, 2019.

In this paper we present new plan for separating association decides that thinks about the time, number of database examines, memory utilization, and the intriguing quality of the guidelines. Find a FIS information mining association calculation that expels the drawbacks of APRIORI calculation and is productive as far as number of database output and time. The incessant examples calculation without hopeful generation dispenses with the exorbitant applicant generation. It likewise abstains from checking the database over and over. Along these lines, we utilize Frequent Pattern (FP) Growth ARM calculation that is increasingly productive structure to mine examples when database develops.

Frequent Pattern, FIS Information Mining, Association Calculation

[1] Agrawal, R., and Psaila, G..,“Active Data Mining. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95)”, 3–8. Menlo Park, Calif.: American Association for Artificial Intelligence, 2012.
[2] Agrawal, R.; Mannila, H.; Srikant, R.; Toivonen, H.; and Verkamo, I, “Fast Discovery of Association Rules. In Advances in Knowledge Discovery and Data Mining, eds”. 2012.
[3] Apte, C., and Hong, S. J. 1996. “Predicting Equity Returns from Securities Data with Minimal Rule Generation. In Advances in Knowledge Discovery and Data Mining”, 2012.
[4] Basseville, M., and Nikiforov, I. V. “Detection of Abrupt Changes: Theory and Application”, Englewood Cliffs, N.J.: Prentice Hall., 2010.
[5] Berndt, D., and Clifford, J..,“Finding Patterns in Time Series: A Dynamic Programming Approach. In Advances in Knowledge Discovery and Data Mining”, 2009.
[6] Brachman, R., and Anand, T.”The Process of Knowledge Discovery in Databases: A Human-Centered Approach”. In Advances in Knowledge Discovery and Data Mining, 37–58, eds
[7] Breiman, L.; Friedman, J. H.; Olshen, R. A.; and Stone, C. J. 1984. “Classification and Regression Trees”, 20007.
[8] Belmont, Calif.: Wadsworth. Brodley, C. E., and Smyth, P. “Applying Classification Algorithms in Practice. Statistics and Computing. Forthcoming”, 2005.

Preetha. S. Raj, Sumitha. V. Dev, "A New Pattern for Extraction of Data using FP Growth ARM Algorithm", International Journal of Computer Sciences and Engineering, Vol.07, Issue.02, pp.19-22, 2019.

Remote sensing is the escalating field in the ultimate modern world. It helps to the society in various aspects. Digital communication reaches the highest level with the help of remote sensing. Water development department works effectively with the remote sensitivity support. Not only has the digital division, Remote sensing also plays a major role in the advancement of food production and agriculture for human development. Agriculture is the backbone for every country. Agriculture development is one of the deciding factors for national development. Agriculture helps to increase domestic production and it leads to eliminating the problems of food shortages. Remote sensing plays a vital role in agriculture development like Crop production forecasting, Assessment of crop damage and crop progress, Crop Identification, Crop acreage estimation and etc. now a day’s crop required level water irrigation and crop disease identification was also done with the help of remote sensing. Every country shows the interest to launch the satellite for agriculture development. This paper helps to analyze the agriculture satellites technology and the remote sensing application for agriculture development.

Remote Sensing, Agriculture, Crop acreage estimation, Crop production forecasting, Agriculture Satellites

[1] M. A. sharifi., “crop inventory and production forecasting using remote sensing and agrometorological models: the case of major agricultural commodities in hamadan province, iran”, International Archives of Photogrammetry and Remote Sensing, Vol. XXXIII, pp.1364-1372, 2000.
[2] Sayan Sau., “Space and time utilization in horticulture based cropping system: an income doubling approach from same piece of land”, journal of Pharmacognosy”, vol. 6(6), pp. 619-624, 2017.
[3] Suresh Kumar Singh, “Analysis of Crop Condition Assessment using Geospatial Technique”, Ph.D thesis, 2015.
[4] Mutlu Ozdogan., “Remote Sensing of Irrigated Agriculture: Opportunities and Challenges”, Remote Sensing, vol.2, PP. 2274-2304, 2010.
[5] Rimjhim Kashyap., “Application of Remote Sensing in Soil Mapping - A Review”, Nort East Students Geo- Congress on Advances in Geotechnical Engineering, pp. 60-66, 2013.
[6] Available at: https://nrsc.gov.in/Drought
[7] A.A. El Baroudy, “ Monitoring land degradation using remote sensing and GIS techniques in an area of the middle Nile Delta, Egypt , 87(2):201-208 • November 2011.
[8] Gohar Ghazaryan., “A rule-based approach for crop identification using multi-temporal and multi-sensor phenological metrics”, Pages 511-524, 2018.
[9] Patrick Trail, “Diagnosing Crop Nutrient Deficiencies in the Field”, ECHO Asia note #29, ECHO Community, pp. 1-7, 2017.
[10] D. Sudha Rani, “Remote Sensing as Pest Forecasting Model in Agriculture”,International Journal of Current Microbiology and Applied Sciences, vol. 7 (03), 2018.
[11] Wenjiang Huang., “Crop Disease and Pest Monitoring by Remote Sensing”, Remote Sensing – Applications, pp. 31-76, 2012.

KR. Sivabalan, E. Ramaraj, "Remote sensing Satellites and its application for agricultural development – Technical Aspect", International Journal of Computer Sciences and Engineering, Vol.07, Issue.02, pp.23-27, 2019.

In this paper, we introduced some new concepts of the quadratic game theory and WSNs with the notions of quadratic game theory. Investigating some of their properties, we show that the formulation of quadratic game theory. For the ancient 20 years, a lot of journalists have motivated their research on wireless sensor networks. Wireless sensor networking is a broad inquiries area, and a lot of investigators have done research in the area of influence good organization to extend network lifetime.

Game Theory, Formulation, and Wireless Sensor Networks

[1]. W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, ”Energy-efficient routing for wireless microsensor networks,” in Proc. 33rd Hawaii Int. Conf. System Sciences(HI CSS), Maui, HI,Jan. 2000.
[2]. S. Megerian, F. Koushanfar, G. Qu, G. Veltri, and M. Potkonjak, “Exposure in wireless sensor networks: Theory and practical solutions,” Wireless Networks, vol. 8, no. 5, pp. 443–454, 2002.
[3]. Sazonov et. al., “Wireless Intelligent Sensor Network for Autonomous Structurals Health Monitoring”, Proc. SPIE, Vol. 5384, 305 (2004)
[4]. Krishnamachari B and Orid F, (2003), Analysis of Energy efficient Fair Routing in Wireless Sensor Network Through Non Linear optimization in the proceedings of workshop on wireless Ad hoc sensor and Wearable Networks in IEEE Vehicular technology Conference, Florida.PP 2844-2848.
[5]. J.H. Chang, L. Tassiulas, Maximum lifetime routing in wireless sensor networks, Journal on IEEE/ACM Transaction and Networking, Vol.12, No.4, pp.609–619, 2004.
[6]. J.H. Chang, L. Tassiulas, Energy conserving routing in wireless ad-hoc networks, in: INFOCOM, 2000, pp. 22–31.
[7]. S. Coleri, P. Varaiya, Fault tolerant and energy efficient routing for sensor networks, in: GlobeCom, 2004.

V. Vinoba, S.M. Chithra, S. Sridevi, "A Study on Quadratic Game Theory formulation using Wireless Sensor Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.02, pp.28-30, 2019.

Association rule mining algorithms are generally used to discover all principles in the database fulfilling some base help and least certainty requirements. So as to diminish the quantity of produced rules, the adjustment of the affiliation rule mining algorithm to mine solitary a specific subset of affiliation rules where the characterization class credit is appointed to one side hand-side was examined in past research. In this examination, a dataset about traffic accidents was gathered from Dubai Traffic Department, UAE. After data preprocessing, Apriori and Predictive Apriori affiliation rules algorithms were connected to the dataset so as to investigate the connection between recorded accidents` variables to mishap seriousness in Dubai. Two arrangements of class affiliation rules were created utilizing the two algorithms and condensed to get the most intriguing standards utilizing specialized measures. Exact outcomes demonstrated that the class affiliation rules produced by Apriori algorithm were more compelling than those created by Predictive Apriori algorithm. More relationship between mishap elements and mishap seriousness level were investigated while applying Apriori algorithm.

Association Rule mining, Apriori Algorithm, Road Accident

[1]. Abdel-Aty, M., and Abdelwahab, H., Analysis and Prediction of Traffic Fatalities Resulting From Angle Collisions Including the Effect of Vehicles‟ Configuration and Compatibility. Accident Analysis and Prevention, 2003.
[2]. Bedard, M., Guyatt, G. H., Stones, M. J., &Hireds, J. P., The Independent Contribution of Driver, Crash, and Vehicle Characteristics to Driver Fatalities. Accident analysis and Prevention, Vol. 34, pp. 717- 727, 2002.
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[6].Kweon, Y. J., &Kockelman, D. M., Overall Injury Risk to Different Drivers: Combining Exposure, Frequency, and Severity Models. Accident Analysis and Prevention, Vol. 35, 2003, pp. 441-450.
[7].Martin, P. G., Crandall, J. R., &Pilkey, W. D., Injury Trends of Passenger Car Drivers In the USA. Accident Analysis and Prevention, Vol. 32, 2000, pp. 541-557.
[8].National Highway Traffic Safety Administration, Traffic Safety Facts 2005, 2007, P. 54.
[9].Ossenbruggen, P.J., Pendharkar, J. and Ivan, J. 2001, “Roadway safety in rural and small urbanized areas”. Accidents Analysis and Prevention, 33 (4), pp. 485– 498.
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[11].Rish, Irina. (2001). "An empirical study of the naive Bayes classifier". IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence.

T. Shobana, R. Rajakumar, "An Analysis the Traffic Accident Using Datamining Technique", International Journal of Computer Sciences and Engineering, Vol.07, Issue.02, pp.31-36, 2019.

Data mining methods are executed in numerous associations as a standard technique for breaking down the vast volume of accessible data, removing valuable data and information to help the real basic leadership forms. Data mining can be connected to wide assortment of utilizations in the instructive division to improve the execution of understudies and additionally the status of the instructive foundations. Instructive data mining is quickly creating as a key method in the examination of data produced in the instructive space. The point of this examination displays an investigation of each semester consequences of UG certificate understudies utilizing data mining strategy. This research work thinks about the outcome characterization algorithms. The correlation is finished utilizing the estimation of precision and estimations of Error Rate. This research work likewise demonstrates what algorithm is most reasonable for anticipating the execution of the understudies among the chose algorithms. The examination work is finished by considering different kinds of algorithm like choice tree algorithm, rule based algorithm, Bayesian algorithm and function based algorithms. This nonexclusive novel methodology can be reached out to different trains too.

Data mining, Classification, Data collection

[1]. Al-Radaideh, Q., Al-Shawakfa, E. and Al-Najjar, M, Mining Student Data Using Decision Trees‟, The 2006 International Arab Conference on Information Technology (ACIT`2006) – Conference Proceedings 2006.
[2]. Ayesha, S. , Mustafa, T. , Sattar, A. and Khan, I. Data Mining Model for Higher Education System‟, European Journal of Scientific Research, vol. 43, no. 1, pp. 24-29. 2010.
[3]. Baradwaj, B. and Pal, S. Mining Educational Data to Analyze Student s‟ Performance‟, International Journal of Advanced Computer Science and Applications, vol. 2, no. 6, pp. 63-69.2011.
[4]. Chandra, E. and Nandhini, K. Knowledge Mining from Student Data‟, European Journal of Scientific Research, vol. 47, no. 1, pp. 156-163.2010.
[5]. El-Halees, A. Mining Students Data to Analyze Learning Behavior: A Case Study‟, The 2008 international Arab Conference of Information Technology (ACIT2008) – Conference Proceedings, University of Sfax, Tunisia, Dec 15-18. 2018.
[6]. Han, J. and Kamber, M. Data Mining: Concepts and Techniques, 2nd edition. The Morgan Kaufmann Series in Data Management Systems, Jim Gray, Series Editor 2016.
[7]. Kumar, V. and Chadha, A. An Empirical Study of the Applications of Data Mining Techniques in Higher Education‟, International Journal of Advanced Computer Science and Applications, vol. 2, no. 3, pp. 80-84.2011.
[8]. Mansur, M. O. Sap, M. and Noor, M. Outlier Detection Technique in Data Mining: A Research Perspective‟, In Postgraduate Annual Research Seminar.2005.
[9]. Romero, C. and Ventura, S. Educational data Mining: A Survey from 1995 to 2005‟, Expert Systems with Applications (33), pp. 135-146.2007.
[10]. Q. A. AI-Radaideh, E. W. AI-Shawakfa, and M. I. AI-Najjar, “Mining student data using decision trees”, International Arab Conference on Information Technology(ACIT`2006), Yarmouk University, Jordan, 2006.
[11]. U. K. Pandey, and S. Pal, “A Data mining view on class room teaching language”, (IJCSI) International Journal of Computer Science Issue, Vol. 8, Issue 2, pp. 277-282, ISSN:1694-0814, 2011.
[12]. Shaeela Ayesha, Tasleem Mustafa, Ahsan Raza Sattar, M. Inayat Khan, “Data mining model for higher education system”, European Journal of Scientific Research, Vol.43, No.1, pp.24-29, 2010.

N. Umarani, R. Rajakumar, "Different Classification Technique using Analysis of Student Academic Dataset", International Journal of Computer Sciences and Engineering, Vol.07, Issue.02, pp.37-43, 2019.

Now a day when we transfer the data, we are using private frequency item sets mining algorithm. It has 2 phases that are pre-processing and mining phase. This algorithm is used for utility, privacy and efficiency the frequency item set is planned which is based on frequency pattern growth algorithm. In pre processing phase consists to improve the privacy, utility and novel smart splitting to transpose database. The mining phase consists to offset the information lost during the transformation splitting information and calculate the runtime estimate for actual support of item set in a given data base . Further the dynamic noise reduction technique is used to reduce the noise at the time of mining phase.

item set, frequent item set mining, differential privacy

[1] Shailza Chaudhary, Pardeep Kumar, Abhilasha Sharma, Ravideep Singh, "Lexicographic Logical Multi-Hashing For Frequent Itemset Mining", International Conference on Computing, Communication and Automation (ICCCA2015)
[2] Lei Xu, Chunxiao Jiang, Jian Wang, Jian Yuan, Yong Ren,"Information Security in Big Data: Privacy and Data Mining", 2014 VOLUME 2, IEEE 29th International Conference on Information Security in Big Data
[3] O.Jamsheela, Raju.G, "Frequent Itemset Mining Algorithms :A Literature Survey", 2015 IEEE International Advance Computing Conference (IACC)
[4] Feng Gui, Yunlong Ma, Feng Zhang, Min Liu, Fei Li, Weiming Shen, Hua Bai, "A Distributed

R. Pandiammal, N. Kavitha, "Effective Algorithm to Find The Frequency Item Sets Using Datamining.", International Journal of Computer Sciences and Engineering, Vol.07, Issue.02, pp.44-46, 2019.