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In software computing, computation is done either deterministic or non-deterministic approach. Deterministic approach includes the constraint like dependency of data in which no random computation is involved. This paper talks about how to achieve more parallelism in context of a deterministic computation approach for the dual-core architecture. In this paper a new Scheduling algorithm which termed as “LA Scheduling” algorithm and its associated component has been presented which is mainly optimised for dual core architecture. Simulation result shows that it helps in reducing the response time of a program and average speedup has been increased.

Parallel Computing, Multicore Architecture, Scheduling Algorithm

[1] Cormen, Thomas H., et al. Introduction to algorithms. Vol. 2. Cambridge: MIT press, 2001.
[2] Karpinski, Marek, and Rutger Verbeek. "On randomized versus deterministic computation." Automata, Languages and Programming. Springer Berlin Heidelberg, 1993. 227-240.
[3] Kahn, Arthur B. "Topological sorting of large networks." Communications of the ACM 5.11 (1962): 558-562.
[4] Lee, Edward A. "The problem with threads." Computer 39.5 (2006): 33-42.
[5] Silberschatz, Abraham, et al. Operating system concepts. Vol. 4. Reading: Addison-Wesley, 1998.
[6] Bosilca, George, et al. "DAGuE: A generic distributed DAG engine for high performance computing." Parallel Computing 38.1 (2012): 37-51.
[7] Intel Corporation https://software.intel.com/en-us/articles/multi-core-processor-architecture-explained October 2008

Shahid Iqbal , "A Deterministic Parallel Computing Approach Optimised for Multicore Architecture," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.58-60, 2015.

In software engineering, graphical user interface testing is the process of testing a product's graphical user interface to ensure that it meets the written specifications. This is normally done through the use of a variety of test cases. To generate a set of test cases, test designers attempt to cover all the functionality of the system and fully exercise the GUI itself. The difficulty in accomplishing this task is twofold: to deal with domain size and with sequences. In addition, the test faces are more difficult in case of regression testing. In this work, we develop a new paradigm for GUI testing, one that we call Observe-Model-Exercise (OME) to tackle the challenges of testing context-sensitive GUIs with undetermined input spaces. Starting with an incomplete model of the GUI’s input space, a set of coverage elements to test, and test cases, OME iteratively observes the existence of new events during execution of the test cases, expands the model of the GUI’s input space, computes new coverage elements, and obtains new test cases to exercise the new elements. The experimental results proves that the proposed work is better than the previously existing works.

[1] C. Bartolini, A. Bertolino, S. Elbaum, and E. Marchetti, “Bringing White-Box Testing to Service Oriented Architectures through a Service Oriented Approach,” J. Systems and Software, vol. 84,pp. 655-668, Apr. 2011.
[2] N.R. Krishnaswami and N. Benton, “A Semantic Model for Graphical User Interfaces,” Proc. 16th ACM SIGPLAN Int’l Conf.Functional Programming (ICFP ’11), pp. 45-57, 2011.
[3] T. Pajunen, T. Takala, and M. Katara, “Model-Based Testing with a General Purpose Keyword-Driven Test Automation Framework,”Proc. IEEE Fourth Int’l Conf. Software Testing, Verification and Validation Workshops (ICSTW), pp. 242-251, Mar. 2011.
[4] David C. Yu, Member Haijun Liu, Student Member Fengjun Wu, “A GUI Based Visualization Tool for Sequence Networks”, IEEE Transactions on Power Systems, Vol-13, No-1,pp.247-263,February 1998.
[5] Atif M. Memon, Martha E. Pollack, and Mary Lou Soffa, “Hierarchical GUI Test Case generation using automated planning”, IEEE transactions on software engineering, vol-27, no-2,pp.453-464, Feb 2001.
[6] Alex Ruiz and Yvonne Wang Price, “Test-Driven GUI Development with testing and abbot”, IEEE transactions on software engineering, vol-28, Issue-4,pp.117-134, Jun 2003.
[7] Atif M. Memon and Qing Xie, “Studying the Fault-Detection effectiveness of GUI test cases for rapidly evolving software”, IEEE transactions on software engineering, vol-31,Issue-10,pp.944-952,October 2005.
[8] Rene´e C. Bryce, Sreedevi Sampath and Atif M. Memon, “Developing a single model and test prioritization strategies for event-driven software”, IEEE transactions on software engineering, vol-37, no-1,pp.812-830,Jan 2011.
[9] Izzat Mahmoud Alsmadi, “Using mutation to enhance GUI testing coverage”, IEEE transaction on Software engineering,vol-1,Issue-2,pp.567-581,Feb 2013.
[10] Xiaoyin Wang, Lu Zhang, Tao Xie, Hong Mei and Jiasu Sun, “Locating need to externalize constant strings for software internalization with generalized string taint analysis”, IEEE transactions on software engineering, vol-39, no-4,pp.245-267,april 2013.

OME;User Interface;Context-sensitive GUIs;Test Case Generation;Quality Concepts, "Testing Event Driven Systems By Using Observe-Model-Exercise Paradigm With Unknown Input Spaces," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.61-64, 2015.

The security is very crucial in developing an e-learning system. Emerging standards for distance learning and education influence in a major way the development of e-learning systems. E-learning system must be secured against manipulation from the side of the students and also it protects user’s privacy. This paper presents a review of e-learning privacy and security requirements.It investigates the more popular e-learning standards to determine their provisions and limitations for privacy and security. The capabilities of a number of existing privacy enhancing technologies, including methods for network privacy, security management, and trust systems, are reviewed and assessed.

E-Learning Privacy, Security Requirements, E-Learning Security

[1] F. Graf, “Providing security for e Learning”, Computers & Graphics, http://dx.doi.org/10.1016/S00978493(02)00062-6, vol. 26, no. 2, (2002) April, pp. 355365.
[2] Ortigosa, “Sentiment analysis in Facebook and its application to e-learning”, Computers in Human Behavior, http://dx.doi.org/10.1016/j.chb.2013.05.024, (2013).
[3] Vladimir I. Zuev, Management Information Systems, Vol. 7 (2012), No. 2, pp. 024-028, Received 12 September 2011 Accepted 24 April 2012 UDC 37.018.43:004.738.5; 371.322:004.
[4] A. YounisAlsabawya, A. Cater-Steel and J. Soar, “IT infrastructure services as a requirement for e-learning system success”, Computers & Education, vol. 69, (2013) November, pp. 431-451.
[5] V. I. Zuev, “e-learning Security Models”, Management Information Systems, vol. 7, no. 2, (2012), pp.024-028.
[6] Karforma Sunil and Ghosh Basudeb ,: On Security issues in e-learning System, “ Proceedings’ of COCOSY-09 ,University Institute of Technology, Burdwan University ,Jan 02-04,(2009).

Meenal Chavan and Poonam Manjare, "E-Learning Security Requirements," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.65-68, 2015.

Automation of descriptive answer evaluation process would be helpful for various universities and academic institution to efficiently handle the assessment of exam answer sheets of students. Our objective is to design an algorithm for the automatic evaluation of multiple sentence descriptive answer. This paper represents an approach to check the degree of learning of the student, by evaluating their descriptive exam answer sheets. By representing the descriptive answer in the form of graph and comparing it with standard answer are the key steps in our approach. In this approach we use pattern matching algorithm for evaluation of answer. .assumption is no grammar checking.

Descriptive Answer, Graphical Representation, Word Net

[1] Hanxiao Shi,Giodong Zhou and Peide Qian (2010),”An Attribute-based Sentiment Analysis System”,Information Technology Journal,PP 1607-1614.
[2] Papri Chakraborty (2012),”Developing an Intelligent Tutoring System for Assessing Students'Cognition and Evaluating Descriptive Type Answer”,IJMER,PP 985-990.
[3] Mita K. Dalal, Mukesh A. Zave (2011),”Automatic Text Classification: A Technical Review”,International Journal of Computer Applications,PP.37-40.

Pranali Nikam , Mayuri Shinde ,Rajashree Mahajan , Shashikala Kadam, "Automatic Evaluation of Descriptive Answer Using Pattern Matching Algorithm," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.69-71, 2015.

Copyright protection and rightful ownership is needed in the fast growing Internet environment. The watermarking offers a convenient way to hide specific information via an imaging system. In this paper, we proposed a IWT and SVD-based image watermarking scheme by embedding the watermark into the host image. The chaos encryption is applied to the watermark image before embedding into host image to provide robustness. The proposed watermarking algorithm is tested for different attacks. It shows very good robustness and also good quality of watermark is extracted by performing other common image processing operations like brightening, sharpening, contrast enhancement etc. The experimental results demonstrate that the proposed method overcomes the various attacks.

IWT, SVD, Chaos

[1] C.-C. Chang, P. Tsai, C.-C. Lin,” SVD-based digital image watermarking scheme”, Pattern Recognition Letters 26 (10) (2005) 1577–1586.
[2] Chesta Jain, Vijay Chaudhary,“ Performance Analysis of Integer Wavelet Transform for Image Compression ” IEEE conference 2011.
[3] Chin-Chin Lai ,”Digital Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition”, IEEE Transaction on Instrumentation and measurement Vol.59, No 11, November 2010.
[4] Li-bao Zhang Xian-chuan Yu, “An Efficient Image Coding Using Multiple Subbands Integer Wavelet Decomposition”, International Symposium on Intelligent Signal Processing and Communication Systems Nov.28-Dec.1, 2007 Xiamen, China
[5] R. Liu, T. Tan, “An SVD-based watermarking scheme for protecting rightful ownership”, IEEE Transactions on Multimedia 4 (1) (2002) 121–128.
6] Ms.K.Thaiyalnayaki, Ms A.Kala, " Dual robust watermarking using Integer wavelet Transform and Singular value decomposition", International Journal of Remote Sensing & Geoscience ISSN No: 2319-3484 ,Volume 2, Issue 6, Nov. 2013
[7] Waleed Al-Nuaimy Mohsen A.M, “An SVD audio watermarking approach using chaotic encrypted images”, Digital Signal Processing 21 (2011) 764–779
[8] F. Han, X. Yu, S. Han, Improved Baker map for image encryption, in: Proceedings of the First International Symposium on Systems and Control in Aerospace and Astronautics, ISSCAA 2006, pp. 1273–1276.
[9] A.Kala, K.Thaiyalnayaki ,"Robust Lossless Image watermarking in Integer Wavelet Domain using SVD", International Journal of Computer Science Engineering,Volume 2,issue 2 ,March 2013.

A.Kala and K.Thaiyalnayaki, "Chaos based Image Watermarking using IWT and SVD," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.72-75, 2015.

Cloud computing is an evolving technology designed to offer a variety of computing and storage services over the Internet. Cloud computing in multimedia is normally correlated with multimedia computing over grids, server-based computing, content delivery and peer to peer multimedia computing. Multimedia services offer a flexible, scalable, efficient data processing technique and it gives a solution for the user demands of best quality multimedia. Since the intelligent mobile phones, wireless networks and tablets have become more popular, network services for the users are no longer limited inside the home. Now public is in the use of mobile devices for watching the multimedia videos via streaming. Not all multimedia file formats are supported by all the mobile devices. Users always want to watch videos or files from everywhere at anytime, regardless of the changes in the network environments. To overcome the problem of limited available bandwidth for mobile streaming, surveyed the previous methods and introduced a network and device-aware QoS method that offers multimedia data which are appropriate for terminal unit. The wastage of bandwidth and terminal power could be avoided by choosing the appropriate transcoding format based on the bandwidth value. QoS method could endow with capable self adaptive multimedia streaming services based on the bandwidth environments.

Cloud multimedia, Adaptive QoS, Network and device aware

[1]Chin-Feng Lai, Honggang Wang, Han-Chieh Chao, and Guofang Nan,”A network and device aware QoS approach for cloud based mobile streaming”, IEEE Trans. multimedia., vol.15, no. 4, june 2013.
[2]Wenwu Zhu, Chong Luo, Jianfeng Wang, and Shipeng Li, “Multimedia cloud computing” in IEEE Signal Process. Mag., vol. 28, no. 3, pp. 59–69, 2011.
[3]K. E. Psannis, Y. Ishibashi, and M.G.Hadjinicolaou, “QoS for wireless interactive multimedia streaming,” in Proc. ACM Workshop QoS and Security for Wireless and Mobile Networks, 2007, pp. 168–171.
[4]S. Ferretti, V. Ghini, F. Panzieri, and E. Turrini, “Seamless support of multimedia distributed applications through a cloud,” in Proc. IEEE 3rd Int. Conf. Cloud Comput. (CLOUD), 2010, pp.
548–549.
[5]H.Choi, J. W.Kang, and J. G. Kim, “Dynamic and interoperable adaptation of SVC for QoS-enabled streaming,”IEEETrans. Consum. Electron., vol. 53, no. 2,pp. 384–385, 2007.
[6]H. Sohn, H. Yoo, Y. B. Lee, C. S. Kim, W. D. Neve, and Y. M. Ro, “MPEG-21 Based scalable bitstream adaptation using medium grain scalability,” in Proc. IEEE Region 10 Conf., 2008, pp. 1–5.
[7]M. F. Tan and X. Su, “Media cloud:When media revolution meets rise of cloud computing,” in Proc. IEEE 6th Int. Symp. Service Oriented Syst. Eng., 2011, pp.
251– 261.
[8]H.Choi, J. W.Kang, and J. G. Kim, “Dynamic and interoperable adaptation of SVC for QoS-enabled streaming,” IEEE Trans. Consum. Electron., vol. 53, no. 2, pp. 384– 385, 200

Mohanakrishnan and M. Azath2, "Survey on Network and Device Aware QoS Approach for Mobile Streaming," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.76-79, 2015.

Wireless Sensor Networks (WSN) is an emerging technology that has the potential to transform the way of human life. Healthcare applications are considered a promising field for Wireless Sensor Network, where the patient’s health can be monitored using Medical Sensors. Wireless Medical Sensor Networks (WMSNs) are the key enabling technology in healthcare applications that allows the wearable biosensors to collect the patient’s vital body parameters to be collected by wearable biosensors. Currently WMSN healthcare research focuses on reliable patient communication, mobility of patient and energy-efficient routing. Security and Privacy protection of the collected data are the major issues to be solved in medical sensor networks. This paper deals with various techniques adopted for securing the medical data during the transmission and also deals with the avoidance of unauthorized access.

Access Control, Data Transmission, Medical Sensor Networks, Security, Privacy

[1] Azzedine Boukerche, and Yonglin Ren,” A Secure Mobile Healthcare System using Trust-Based Multicast Scheme”, IEEE Journal On Selected Areas In Communications, Vol. 27, No. 4, May 2009,316-325.
[2] Daojing He, Sammy Chan, Member, IEEE, and Shaohua Tang, Member, IEEE,” A Novel and Lightweight System to Secure Wireless Medical Sensor Networks”, IEEE Journal Of Biomedical And Health Informatics, Vol. 18, No. 1, January 2014,23-32.
[3] Denis Trcek And Andrej Brodnik, University Of Ljubljana,” Hard And Soft Security Provisioning for Computationally Weak Pervasive Computing Systems In E-Health”, IEEE Wireless Communications August 2013,45-53.

[4] Geoffrey G. Messier and Ivars G. Finvers,” Traffic Models for Medical Wireless Sensor Networks”, IEEE Communications Letters, Vol. 11, No. 1, January 2007 ,21-30.
[5] Oscar Garcia-Morchon, Thomas Falck, Tobias Heer, Klaus Wehrle,”Security for Pervasive Medical Sensor Networks”, Vol.12, No.2, June 5th 2009,126-134.
[6] Rongxing Lu, Member, IEEE, Xiaodong Lin, Member, IEEE, and Xuemin (Sherman) Shen, Fellow, IEEE,” SPOC: A Secure and Privacy-preserving Opportunistic Computing Framework for Mobile-Healthcare Emergency”, IEEE Transactions On Parallel And Distributed Systems, Vol. 12, No. 2, May 2012,452-461.
[7] Shu-Di Bao, Student Member, IEEE, Carmen C. Y. Poon, Student Member, IEEE,Yuan-Ting Zhang, Fellow, IEEE, and Lian-Feng Shen,” Using the Timing Information of Heartbeats as an Entity Identifier to Secure Body Sensor Network”, IEEE Transactions On Information Technology In Biomedicine, Vol. 12, No. 6, November 2008,155-162.
[8] S. Moller, T. Newea, S. Lochmannb," Prototype of a secure wireless patient monitoring system for the medical Community”, 2011 Elsevier B.V. All rights reserved.
[9] Vishwa Goudar and Miodrag Potkonjak,” A Robust Watermarking Technique for Secure Sharing of BASN Generated Medical Data”, 2014 IEEE International Conference on Distributed Computing in Sensor Systems.
[10] Zhaoyang Zhang, Honggang Wang, Athanasios V. Vasilakos, and Hua Fang,” ECG- Cryptography and Authentication in Body Area Networks”, IEEE Transactions On Information Technology In Biomedicine, Vol. 16, No. 6, November 2012,321-332.

C. Gayathri and D.Sathya, "A Survey on Major Security Issues for Health Data in Wireless Medical Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.80-84, 2015.

the mean of the paper is to apply computer vision technique to transmit secret messages from sender to receiver. Cryptography and steganography are the two techniques to transform secret messages. In this paper steganography technique is used. Steganography is the hiding and transmitting secret data to the authorised user. One of the hiding techniques utilised here is LSB manipulation. System generate 3 keys using AES 128 bit algorithm based on these keys ,sender and receiver can encrypt and decrypt secret messages respectively. MySQL server, net beans IDE tool is used to obtain results.

Cryptography, LSB, AES, Steganography

[1] X. Zhang, “Separable Reversible Data Hiding in Encrypted Image” IEEE Trans. Inform. Forensics Security, vol. 7, no. 2, pp. 826-832, April 2012.
[2] Akash Kumar Mandal, Chandra Parakash, Mrs.Archana Tiwari “Performance Evaluation of Cryptographic Algorithms: DES and AES”, IEEE Trans. on Electrical, Electronics and Computer Science, 2012.
[3] X. Zhang, “Lossy compression and iterative reconstruction for encrypted image,” IEEE Trans. Inform. Forensics Security, vol. 6, no. 1, pp. 53–58, Feb. 2011.
[4] MazharTayel, HamedShawky, Alaa El-Din Sayed Hafez, ”A New Chaos Steganography Algorithm for Hiding Multimedia Data” Feb. 19~22, 2012 ICACT2012.
[5] T. Bianchi, A. Piva, and M. Barni, “On the implementation of the discrete Fourier transform in the encrypted domain,” IEEE Trans. Inform. Forensics Security, vol. 4, no. 1, pp. 86–97, Feb. 2009.

Malatesh M, Smt. Anitha G and Ujjini Venkatesh, "Secure Data Transform in Encrypted Image Using Steganography Technique," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.85-89, 2015.

Passwords are the powerful tools that tend to keep all data and information digitally safe. It is frequently noticed that text password remains predominantly popular over the other formats of passwords, due to the fact that it is simple and expedient. However, text passwords are not always sturdy enough and are very easily stolen and misused under different vulnerabilities. Other persons can obtain a text password when a person creates a weak password or a password that is completely reused in many sites. In this condition if one password is hacked, it can be used for all the websites. This is called the Domino Effect. Another unsafe situation is when a person enters his/her password in a computer that is not trust-worthy; the password is prone to stealing attacks such as phishing, malware and key loggers etc. Among the most significant current threats to online banking are keylogging and phishing. These attacks extract user identity and account information to be used later for unauthorized access to user’s financial accounts. This paper focuses on user authentication protocols which are used for secured online processing like online banking. During recent years numbers of authentication protocols are proposed in this area. For further researches, understanding of these approaches is essential.

Network Security, User Authentication, Password Reuse Attack, Password Stealing Attack

[1]. B. Ives, K. R. Walsh, and H. Schneider, “The domino effect of password reuse,” Commun. ACM, vol. 47, no. 4, 2004, pp. 75–78..
[2]. M.Wu, S. Garfinkel, and R. Miller, “Secure web authentication with mobile phones,” in DIMACS Workshop Usable Privacy Security Software, Citeseer, 2004.
[3]. M. Mannan and P. van Oorschot, “Using a personal device to strengthen password authentication from an untrusted computer,” Financial Cryptography Data Security, 2007, pp. 88–103.
[4]. C. Yue and H. Wang, “SessionMagnifier: A simple approach to secure and convenient kiosk browsing,” in Proc. 11th Int. Conf. Ubiquitous Computing, ACM, 2009, pp. 125–134.
[5]. B. Parno, C. Kuo, and A. Perrig, “Phoolproof phishing prevention,” Financial Cryptography Data Security, 2006, pp. 1–19.
[6]. B. Schneier, “Two-Factor Authentication: Too Little, Too Late,” in Inside Risks 178, Communications of the ACM, 48(4), April 2005.
[7]. S. Gawand E. W. Felten, “Password management strategies for online accounts,” in SOUPS ’06: Proc. 2nd Symp. Usable Privacy . Security, New York, ACM, 2006, pp. 44–55.
[8]. W.C. Kuo, Y.C. Lee, “Attack and improvement on the one-time password authentication protocol against theft attacks”, Proc. of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, Aug. 2007, pp.19-22.
[9]. Hung-Min Sun, Yao-Hsin Chen, and Yue-Hsun Lin ,”oPass: A User Authentication Protocol Resistant to Password Stealing and Password Reuse Attack”, in IEEE Transactions On Information Forensics And Security, Vol. 7, No. 2, April 2012.
[10]. Anand Sharma and Vibha Ojha. “Password based authentication” Philosophical Survey, IEEE. 2010.
[11]. D. Florencio and C. Herley, “A large-scale study of web password habits,” in WWW ’07: Proc. 16th Int. Conf. World Wide Web., New York,, ACM, 2007, pp. 657–666.
[12]. S. Chiasson, A. Forget, E. Stobert, P. C. van Oorschot, and R. Biddle, “Multiple password interference in text passwords and click-based graphical passwords,” in CCS ’09: Proc. 16th ACM Conf. Computer Communications Security, New York, 2009, pp. 500–511.
[13]. I. Jermyn, A. Mayer, F. Monrose, M. K. Reiter, and A. D. Rubin, “The design and analysis of graphical passwords,” in SSYM’99: Proc. 8th Conf. USENIX Security Symp., Berkeley, CA, USENIX Association, 1999 pp. 1–1.
[14]. B. Pinkas and T. Sander, “Securing passwords against dictionary at- tacks,” in CCS ’02: Proc. 9th ACM Conf. Computer Communications Security, New York, ACM, 2002, pp. 161–170.
[15]. H. Tian, X. Chen, and Y. Ding, “Analysis of Two Types Deniable Authentication Protocols,” I. J. Network Security, Jul. 2009, pp. 242-246.

Prajitha M V, "A Survey on User Authentication Protocols," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.90-96, 2015.

We live in a time where the need for data mining is prevalent for extracting knowledge and understanding patterns, given the vast amount of data being generated. Clustering is one of the many data mining functionalities which divide data into groups containing similar data objects Classification is a technique used for discovering classes of unknown data. Before applying any mining technique, irrelevant attributes needs to be filtered. Filtering is done using different feature selection techniques like wrapper, filter and embedded technique. This paper is an introductory paper on different techniques used for classification and clustering.

Data Mining, Clustering Techniques, Classification Techniques

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[2] A. Darwiche, Modeling and Reasoning with Bayesian Networks, Cambridge University Press, 2009
[3] G.F. Cooper, P. Hennings-Yeomans, S. Visweswaran and M. Barmada, “An Efficient Bayesian Method for Predicting Clinical Outcomes from Genome-Wide Data”, AMIA 2010 Symposium Proceedings, 2010, pp. 127-131
[4] M. Garofalakis, D. Hyun, R. Rastogi and K. Shim, “Building Decision Trees with Constraints”, Data Mining and Knowledge Discovery, vol. 7, no. 2, 2003, pp. 187 – 214
[5] T.M. Mitchell, Machine Learning, McGraw-Hill Companies, USA, 1997
[6] Y. Singh Y, A.S. Chauhan, “Neural Networks in Data Mining”, Journal of Theoretical and Applied Information Technology, 2005, pp. 37-42
[7] Z. Pawlak, “Rough sets”, International Journal of Computer and Information Sciences, 1982, pp. 341- 356
[8] L. Tari, C. Baral and S. Kim, “Fuzzy c-means clustering with prior biological knowledge”, Journal of Biomedical Informatics, 42(1), 2009, pp. 74-81
[9] N.N. Karnik, J.M. Mendel and Q. Liang, “Type-2 Fuzzy Logic Systems”, IEEE Transactions on Fuzzy Systems, Vol. 7, No. 6, 1999, 643-658
[10] J.R. Castro, O. Castillo and L.G. Martínez, “Interval Type-2 Fuzzy Logic Toolbox”, Engineering Letters 15(1), 2007, pp. 89-98
[11] Lan Yu, “Applying Clustering to Data Analysis of Physical Healthy Standard”, 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2010), pp. 2766-2768.
[12] Yun Ling and Hangzhou, “Fast Co-clustering Using Matrix Decomposition”, IEEE 2009 Asia- Pacific Conference on Information Processing, pp. 201-204.
[13] Vignesh T. Ravi and Gagan Agrawal, “Performance Issues in Parallelizing Data-Intensive Applications on a Multi-core Cluster”, 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 308-315.
[14] David Pettinger and Giuseppe Di Fatta, “Scalability of Efficient Parallel K-Means”, IEEE e- Science 2009 Workshops, pp. 96-101.
[15] Madjid Khalilian, Farsad Zamani Boroujeni, Norwati Mustapha, Md. Nasir Sulaiman, “K-Means Divide and Conquer Clustering”, IEEE 2009, International Conference on Computer and Automation Engineering, pp. 306-309.
[16] F. Yang, T. Sun, C. Zhang, An efficient hybrid data clustering method based on K-harmonic means, and Particle Swarm Optimization, Expert Systems with Applications 2009, pp. 9847–9852.

Vishakha D. Charhate and Poonam A. Manjare, "An Analysis of Classification and Clustering Techniques used in Data Mining," International Journal of Computer Sciences and Engineering, Vol.3, Issue.1, pp.97-100, 2015.