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Anomaly discovery in high-dimensional information presents different difficulties coming about because of the "scourge of dimensionality." A common view is that separation fixation, i.e., the propensity of separations in high-dimensional information to wind up garbled, blocks the location of anomalies by making separation based strategies name all focuses as similarly great exceptions. In this paper, we give confirm supporting the conclusion that such a view is excessively straightforward, by exhibiting that separation based strategies can deliver all the more differentiating exception scores in high-dimensional settings. By assessing the great k-NN technique, the density-based local anomaly factor and impacted frameworks strategies, and anti-hub strategies with respect to different manufactured and genuine informational collections, we offer novel knowledge into the value of turn around neighbor checks in unsupervised exception recognition.

High-Dimensional Data, Anomaly Detection, Reverse Nearest Neighbors (RNN), Distance Concentration

[1] V.Chandola, et al, “Anomaly detection: A survey”, ACM /computSuro, vol 41,no. 3,p. 15,20090
[2] A. Zimek, et al, “A survey on unsupervised outlier detection in high-dimensional numerical data,” Statist. Anal. Data Mining, vol. 5, no. 5, 2012
[3] C. C. Aggarwal et al, “Outlier detection for high dimensional data,” in Proc. 27th ACM SIGMOD Int. Conf. Manage. Data, 2001,
[4] Srinivasa Rao, “A Review on Multivariate Mutual Information”, University of Notre Dame, vol. 2, 2005
[5] Shu Wu, et al, “Information-Theoretic Outlier Detection for Large-Scale Categorical Data”, IEEE Explorer vol. 25, No. 3.
[6] Markus M. et al, “Institute for computer science. Department of Computer Science” University of British Columbia.
[7] A. Hinneburg, et al, “On the surprising behavior of distance metrics in high dimensional spaces,” in Proc 8thIntConf on Database Theory (ICDT), 2001.
[8] Jayshree S.Gosavi, http://www.rroij.com
[9]Random key algorithm https://dzone.com/articles/random-number-generation-in-java
[10]KNN-Algorithm http://www.saedsayad.com/k_nearest_neighbors.htm

M. Tejasri, K. Sri Lakshmi, K. Gowri Raghavendra Narayan, "Unsupervised Distance-Based Anomaly disclosure in RNN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.439-441, 2018.

A process of partitioning a set of data (or objects) into a set of significant sub-classes, called clusters. Also can be said as unsupervised classification which has no predefined classes. K- Mean is a type of unsupervised learning, which is used when we have any data without defined class or groups. The goal of this algorithm is to find the number of groups in the data, and represented by the variables K1, K2, up till KN. A wavelet based clustering approach for spatial statistics on very huge data. This is a grid based approach which applies wavelet transform in the quantized trait space and then senses the dense section in the transformed space. This paper discusses about the review of the clustering techniques which are observed and used by other numerous researchers for data mining. Further, this paper discusses about the advantages and limitations of the clustering techniques. As based on previous researchers’ contribution that k-mean alone can’t be efficient enough for the increased data set. So with time improved versions were introduced and combining two or more techniques for data mining clustering was practiced. This paper overcame the limitations and found more efficient way for clustering.

Data Mining, Clustring, K-Means Clustering, Wave Clustering

[1]. Jitendra Kumar and Binit Kumar Sinha ID CODE-1789, Department of Computer Science (NIT ROURKELA, ODISHA) Privacy Preserving Clustering in Data Mining
[2]. Michail Vlachos, Jessica Lin, Eamonn Keogh and Dimitrios Gunopulos Computer Science & Engineering Department University of California - Riverside A Wavelet-Based Anytime Algorithm for K-Means Clustering of Time Series.
[3]. Shruti Dalmiya, Avijit Dasgupta and Soumya Kanti Datta International Journal of Computer Applications (0975-8887) Application of Wavelet based K-means Algorithm in Mammogram Segmentation.
[4]. Rafal Ladysz FINAL PROJECT PAPER for INFS 795 CLUSTERING OF EVOLVING TIME SERIES DATA
[5]. Kondra, Janardhan Reddy Privacy Preserving Optics Clustering ID CODE-8539, Department of Computer Science (NIT ROURKELA, ODISHA)
[6]. Bikash Sharma and Aman Jain Privacy preserving data mining ID CODE -4218, Department of Computer Science (NIT ROURKELA, ODISHA)
[7]. Kaur, S., Chaudhary S., Bishnoi N. (2015) a survey: Clustering Algorithm in Data Mining. International Journal of Computer Applications, (0975-8887), 12-14.
[8]. Vaidya J., Clifton C. (2003) Privacy Preserving K-Means Clustering Over Vertically Partitioned Data. SIGKDD. 206-215.
[9]. Sachin Shinde et al (2003) Improved K-means Algorithm for Searching Research Papers, International Journal of Computer Science & Communication Networks,Vol (6),197-202
[10]. Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman and Angela Y. Wu (2002) An Efficient k-Means Clustering Algorithm:Analysis and Implementation IEEE transactions on pattern analysis and machine intelligence, vol. 24, no. 7, july 2002.
[11]. Christopher and Divya (2005) A Study of Clustering Based Algorithm for Outlier Detection in Data streams. International. Journal of Advanced Networking and Applications, Proceedings of the UGC Sponsored National Conference on Advanced Networking and Applications, 194-197.
[12]. Na S., XuminL., Yong G.(2010), Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm, IITSI `10 Proceedings of the 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, 63-67.
[13]. Kedar B. Sawant Shree Rayeshwar (2015) Institute of Engineering and Information Technology IT Department, Shiroda-Goa Volume 3, Issue 1, 2015, ISSN 2349-4395 (PRINT) & ISSN 2349-4409 (ONLINE).
[14]. Priti Maheshwary et al. (2011) International Journal on Computer Science and Engineering (IJCSE) ISSN : 0975-3397 Vol. 3 No. 2
[15]. Kalaivani. R and Dr. R. Manicka Chezhian (2013) A Competent Data Set Grouping in Clustering Algorithms Volume 3, Issue 8, August 2013 ISSN: 2277 128X
[16]. Dr. S. Vijayarani, Ms.P.Jothi (2014) Partitioning Clustering Algorithms for Data Stream Outlier Detection. International Journal of Innovative Research in Computer and Communication Engineering. ISSN (Online): 2320-9801
[17]. Gholamhosein Sheikholeslami, Surojit Chatterjee, Aidong Zhang WaveCluster: a wavelet-based clustering approach for spatial data in very large databases. The VLDB Journal (2000) 8: 289–304
[18]. Ling Chen, Ting Yu and Rada Chirkova (2015) WaveCluster with Differential Privacy Department of Computer Science, North Carolina State University, Raleigh, USA.
[19]. Ahmet Artu Yıldırım and Cem Özdoğan Parallel WaveCluster: A linear scaling parallel clustering algorithm implementation with application to very large datasets J. Parallel Distrib. Comput. 71 (2011) 955–962
[19]. K. Chitra and Dr. D.Maheswari (2017) International Journal of Computer Science and Mobile Computing ISSN 2320–088X

Manjot Kiran Kaur Bedi, Rekha Bhatia, "A Comprehensive Review of Privacy Preserving Framework Using Wavecluster and K-Means Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.442-446, 2018.

Information Technology plays an important role in the future improvement of financial sectors. The progression of Information and Communication technology has brought plenty of changes in all circles of the daily life of an individual. Electronic commerce, generally written as e-commerce includes an online transaction. E-commerce is purchasing and selling of items and services over the Internet by organizations and buyers. The various technologies that are drawn from E-commerce include Internet marketing, supply chain management, mobile commerce, online transaction processing, Electronic Data Interchange (EDI), mobile commerce and automated data collection systems. Due to the demand of e-commerce both globally and regionally, there is a huge increase in the exchange of products and services. The benefits of e-commerce have increased the value of consumer loyalty`s regarding client comfort in any place and empowers the organization to acquire a more competitive advantage over alternate contenders. This paper attempts to describe the principal growth factors required for E-commerce and highlight the various challenges faced by e-commerce in India. This paper also describes the different security issues in E-commerce.

E-Commerce, Electronic Data Interchange, Consumer, Challenges, Security

[1] Dr.Rajasekar, S. Agarwal, “A Study on Impact of E-Commerce on India’s Commerce”, International Journal of Development Research , Vol. 6, Issue, 03, pp. 7253-7256, March, 2016.
[2] Dr. Rajesh Sharma. “Challenges and Opportunities of E-Commerce and Its Role in Management Education in India”, International Journal of Current Trends in Engineering & Technology Volume: I, Issue: I (Nov - Dec. 2014).
[3] Chaithralaxmi.T and Shruthi. N. “E-Commerce in India – Opportunities and Challenges”, International Journal of Latest Trends in Engineering and Technology SACAIM 2016, pp. 505-510.
[4] Niranjanamurthy M, DR. Dharmendra Chahar. “The study of E-Commerce Security Issues and Solutions”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 7, July 2013.
[5] https://www.kartrocket.com/blog/ecommerce-growth-india-market-research-stats/
[6] https://www.linkedin.com/pulse/e-commerce-india-scope-challenges-future-shantanu-gaur

Manasha Saqib, "The Challenges and Security Issues Faced by E-Commerce in India: A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.447-449, 2018.

Creativity is the major source that creates the impact on users what the product is and what the content is, the bisociation has the capacity to connect the dissimilar components and the edge of the bisociation we apply the cognitive learning, as the cognitive learning has the byproduct of learning the theme on its own methods that have the capacity of artificial intelligence to maintain the legacy of brining the new ideas and the innovation to the sciences now. The main theme of this survey is based on how the genetic theory with the help of neural network get attached to the study of Iot the watch can sense the information when the person in critical zone and reports the nature and position through GPS. The paper focus mainly on thinking ability and innovation about the types that mature the concept of Iot with smart watch that sense with biological flavor.

Bisociation, Cognitive Learning, GPS, SOS, Smart watches, IOT

[1] Koestler, A. (1964). The act of creation
[2] A. Helen , M. Fathima Fathila, R. Rijwana, Kalaiselvi .V.K.G]A Smart Watch For Women Security
Based On Iot Concept ‘watch me’978-1-5090-6221-8/17/$31.00 2017 ieee
[3] Dongare Uma, Vyavahare Vishakha and Raut Ravina, “An Android Application for Women Safety Based on Voice Recognition”, Department of Computer Sciences BSIOTR wagholi, Savitribai Phule Pune University India, ISSN 2320–088X International Journal of Computer Science and Mobile Computing (IJCSMC) online at www.ijcsmc.com,Vol.4 Issue.3, pg. 216-220, March- 2015
[4] MAGESH KUMAR.S and RAJ KUMAR.M, “IPROB – EMERGENCY APPLICATION FOR WOMEN”, Department of Computer science Sree Krishna College of Engineering Unai village Vellore (TN) India, ISSN 2250-3153 International Journal of Scientific and Research Publications, online at the link www.ijsrp.org , Volume 4, Issue 3, March 2014.International Journal of Computer Applications (0975 – 8887)
Volume 130 – No.11, November201540
[5] Vaijayanti Pawar, Prof. N.R.Wankhade, Dipika Nikam, Kanchan Jadhav and Neha Pathak, “SCIWARS Android Application for Women Safety”, Department of Computer Engineering, Late G.N.S.COE Nasik India, ISSN: 2248-9622 International Journal of Engineering Research and Applications Online at the link www.ijera.com, Volume 4, Issue 3(Version 1), pp.823-826, March 2014.
[6] Bhaskar Kamal Baishya, “Mobile Phone Embedded With Medical and Security Applications”, Department of Computer Science North Eastern Regional Institute of Science and Technology Nirjuli Arunachal Pradesh India, e-ISSN: 2278-0661 p- ISSN: 2278-8727 IOSR Journal of Computer Engg (IOSR-JCE) www.iosrjournals.org, Volume 16, Issue 3 (Version IX ), PP 30-3, May-Jun. 2014.
[7] Dr. Sridhar Mandapati, Sravya Pamidi and Sriharitha Ambati, “A Mobile Based Women Safety Application (I Safe Apps)”, Department of Computer Applications R.V.R & J.C College of Engineering Guntur India, e-ISSN: 2278-0661, p-ISSN: 2278-8727, IOSR Journal of Computer Engg (IOSR-JCE) www.iosrjournals.org, Volume 17, Issue 1 (Version I), PP 29-34, Jan.–Feb. 2015
[8] THOOYAVAN V, “ADVANCED SECURITY SYSTEM FOR WOMEN”, Department of ECE Vidyaa Vikas College of Engineering and Technology Vasai Thane India, Final year project, Serial number HEM 128 IEEE 2014 Project List under real time target surveillance system, slides share on www.slideshare.net, Jun 24, 2014.
[9] Prof. Basavaraj Chougula, Archana Naik, Monika Monu, Priya Patil and Priyanka Das “SMART GIRLS SECURITY SYSTEM”, Department of Electronics and telecommunication KLE’s College of Engineering and Technology Belgaum India, ISSN 2319 – 4847 International Journal of Application or Innovation in Engineering & Management (IJAIEM) Web Site: www.ijaiem.org, Volume 3, Issue 4, April 2014.
[10] Nishant Bhardwaj and Nitish Aggarwal, “Design and Development of “Suraksha”-A Women Safety Device”, Department of Electronics and Communication ITM UNIVERSITY Huda Sector 23-A Gurgaon Delhi India, ISSN 0974-2239 International Journal of Information & Computation Technology online available at http://www. irphouse.com, Volume 4, pp. 787-792, November 2014.
[11] Poonam Bhilare, Akshay Mohite, Dhanashri Kamble, Swapnil Makode and Rasika Kahane, “Women Employee Security System using GPS And GSM Based Vehicle Tracking”, Department of Computer Engineering Vishwakarma IOT Savitribai Phule Pune University India, E-ISSN:-2349-7610 INTERNATIONAL JOURNAL FOR RESEARCH IN EMERGING SCIENCE AND TECHNOLOGY, Volume-2, ISSUE-1, JAN-2015.

H venkateshwara Reddy, Ravula Arun Kumar, G Mallikarjun Reddy, "A survey on Applicability of bisociation and cognitive learning on Iot based smart watches and SOS Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.450-456, 2018.

The major objective of any communication system is to have the messages dispatched to their corresponding terminal. The network topology of DTNs/OMNs is not only highly dynamic, but also exhibits high degree of network partitioning. The maneuverability of the node in delay tolerant network considerably harms the productivity of data forwarding. The proposed approach selects the path according to the feasible number of hops needed to reach the corresponding node. The architecture anonymizes each and every node of the network by giving them fake ids. When a node wants to send its confidential data to the other node then the sender node and receiver node only knows each other’s real id. Here the data is send in the encrypted form which can be decrypted using a key.

Disruption tolerant network, multicast, privacy, security, unicast

[1] Kang Chen, Member and Haiying Shen, “Face Change: Attaining Neighbor Node Anonymity in Mobile Opportunistic Social Networks With Fine-Grained Control” in IEEE/ACM Transactions on Networking(TON), vol. 25 issue 2, April 2017
[2] K. Chen and H. Shen, “Fine-grained encountering information collection under neighbor anonymity in mobile opportunistic social networks,” in Proc. IEEE ICNP, Nov. 2015, pp. 179–188.
[3] W. Gao and Q. Li, “Wakeup scheduling for energy-efficient communication in opportunistic mobile networks,” in Proc. IEEE INFOCOM, Apr. 2013, pp. 2058–2066.
[4] A. Balasubramanian, B. Levine, and A. Venkataramani, “DTN routing as a resource allocation problem,” in Proc. SIGCOMM, 2007, pp. 373–384.
[5] K. Chen, H. Shen, and H. Zhang, “Leveraging social networks for p2p content-based file sharing in disconnected MANETs,” in IEEE Trans. Mobile Comput., vol. 13, no. 2, pp. 235–249, Feb. 2014.
[6] A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass, and J. Scott, “Impact of human mobility on opportunistic forwarding algorithms” IEEE Trans. Mobile Comput., vol. 6, no. 6, pp. 606–620, Jun. 2007.
[7] M. C. Chuah, “Social network aided multicast delivery scheme for human contact-based networks,” in Proc.1st Simplex, vol.1, issue 3, Feb. 2009.
[8] V. Conan, J. Leguay, and T. Friedma n, “Characterizing pairwise inter-contact patterns in delay tolerant networks,” in Proc. 1st Int. Conf. Autonomic Comput. Commun. Syst., Article no. 19, Oct. 2007.
[9] P. Costa, C. Mascolo, M. Musolesi, and G. Picco, “Socially-aware routing for publish-subscribe in delay-tolerant mobile ad hoc networks” in IEEE J. Sel. Areas Commun., vol. 26, no. 5, pp. 748–760, Jun.2008.
[10] Junggab Son, Donghyun Kim, Rahman Tashakkori, Alade O, Tokuta, Heekuck Oh, “A New Mobile Online Social Network Based Location Sharing with Enhanced Privacy Protection” in IEEE Computer Communication and Networks (ICCCN), 25th International Conference, Aug. 2016.
[11] N. Eagle and A. Pentland, “Reality mining: Sensing complex social systems,” in Pers. Ubiquitous Comput., vol. 10, no. 4, pp. 255–268, 2006.
[12] E. Daly and M. Haahr, “Social network analysis for routing in disconnected delay-tolerant MANETs,” in Proc. ACM MobiHoc, 2007, pp.32–40.
[13] V. Erramilli, A. Chaintreau, M. Crovella, and C. Diot, “Delegation for-warding,” in Proc. ACM MobiHoc, 2008, pp. 251–260.
[14] K. Fall, “Delay tolerant network architecture for challenged internets,” in Proc. ACM SIGCOMM, 2003, pp. 27–34.
[15] L. Freeman, “A set of measures of centrality based on betweenness, Sociometry”, in IEEE vol. 40, no. 1, pp. 35–41, 1977.
[16] W. Gao and G. Cao, “Fine-grained mobility characterization: Steady and transient state behaviors,” in Proc. ACM MobiHoc, 2010, pp.61–70.
[17] W. Gao and G. Cao, “On exploiting transient contact patterns for data forwarding in delay tolerant networks,” in Proc. IEEE ICNP, 2010, pp.193–202.
[18] W. Gao and G. Cao, “User-centric data dissemination in disruption tolerant networks,” in Proc. IEEE INFOCOM, 2011, pp. 3119–3127.
[19] W. Hsu and A. Helmy, “On nodal encounter patterns in wireless LAN races” in IEEE Trans. Mobile Comput., vol. 9, no. 11, pp. 1563–1577, Nov. 2010.
[20] W. Gao, Q. Li, B. Zhao, and G. Cao, “Multicasting in delay tolerant networks: A social network perspective,” in Proc. ACM MobiHoc, 2009, pp. 299–308.
[21] P. Hui, J. Crowcroft, and E. Yoneki, “Bubble rap: Social-based for-warding in delay tolerant networks,” in Proc. ACM MobiHoc, 2008, pp. 241–250.

A.S. Jaiswal, R. Welekar, "Fine-Grained Control for Neighbour Node Anonymity in Opportunistic Mobile Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.457-460, 2018.

Clustering is important part in Data mining. Clustering is a technique, in which data is using in the form of clusters. A set of objects divided into groups these groups called clusters. K-MEANS is a basic type of clustering technique. It is an unsupervised learning. K-means clustering is a simple technique, which is use to group items into k clusters. BIRCH is one of the famous methods, which used with the k-means to improve the quality of data, which are present in clusters. BIRCH is an (Balanced Iterative Reducing and Clustering using Hierarchies). Birch is a scalable clustering method, which mainly designed for very large data sets. In this paper we discussed about review of other clustering technique which are used by others researchers for data mining. We also discussed the limitations and applications of clustering techniques, which are most popular for data mining. This paper also represents a current review about the K-MEANS and BIRCH algorithm.

Data Mining, Clustring, K-Means Clustering, Birch Clustering

[1] Shraddha Shukla and Naganna (2014)S, “A Review ON K-means DATA Clustering APPROACH” International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 17 (2014), pp. 1847-1860
[2] Wang, X. Y., & Garibaldi, J. M. (2005, June) “A comparison of fuzzy and non-fuzzy clustering techniques in cancer diagnosis” In Proceedings of the 2nd International Conference in Computational Intelligence in Medicine and Healthcare, BIOPATTERN Conference, Costa da Caparica, Lisbon, Portugal (Vol. 28).
[3] Kanungo, Tapas, et al. "An efficient k-means clustering algorithm: Analysis and implementation.” IEEE transactions on pattern analysis and machine intelligence 24.7 (2002): 881-892.
[4]Rafsanjani, Marjan Kuchaki, Zahra Asghari Varzaneh, and Nasibeh Emami Chukanlo. "The Journal of Mathematics and Computer Science." TJMCS Vol .5 No.3 (2012) 229-240
[5] Christopher, T and Divya, T. (2015). “A Study of Clustering Based Algorithm for Outlier Detection in Data streams”. International Journal of Advanced Networking and Applications, Proceedings of the UGC Sponsored National Conference on Advanced Networking and Applications, 194-197.
[6]Kaur, S., Chaudhary S., Bishnoi N. (2015). “A Survey: Clustering Algorithms in Data Mining.” International Journal of Computer Applications, (0975- 8887), 12-14.
[7]Vijayarani S., Jothi P.,(2014). “Hierarchical and Partitioning Clustering Algorithms for Detecting Outliers in Data Streams”. International Journal of Advanced Research in Computer and Communication Engineering, 3(4), 6204-6207.
[8] Fichtenberger H., Gillé M., Schmidt M., Schwiegelshohn C., Sohler C. (2013) “BICO: BIRCH Meets Coresets for k-Means Clustering. In: Bodlaender H.L., Italiano G.F. (eds) Algorithms”. ESA 2013. Lecture Notes in Computer Science, vol 8125. Springer, Berlin, Heidelberg
[9]Na S., XuminL. ,Yong G.(2010), “Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm”. Third International Symposium on Intelligent Information Technology and Security Informatics, pp.63-67
[10] Kumar J. and Sinha B.K. (2010), “Privacy Preserving Clustering In Data Mining”, A thesis submitted for degree of bachelor in Technology in Computer science and engineering of Department of Computer Science and Engineering ,National Institute of Technology Rourkela
[11] Vaidya J.,Clifton C. (2003). “Privacy-Preserving K -Means Clustering over Vertically Partitioned Data”. KDD-2003 Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. SIGKDD. 206-215
[12] Zhang, T., Ramakrishnan, R. & Livny, M. “BIRCH: A New Data Clustering Algorithm and Its Applications” Data Mining and Knowledge Discovery(1997) , Volume 1, Issue 2, pp 141–182
[13]Raghu Ramakrishnam Zhang, “BIRCH: A New Data Clustering Algorithm and Its Applications” Data Mining and Knowledge Discovery(1997) , Volume 1, pp 141–182
[14] Kedar B. Sawant Shree Rayeshwar “international journals of advances in management and enginerring sciences, volume 4, issue 6(1)RJanuary 2015, PP 22-27 ISSN 2349-4395(Print) & ISSN2349-4409(Online)
[15] Prateeksha Tomar, Amit Kumar Manjhvar, “Clustering Classification for Diabetic Patients using K-Means and M-Tree prediction model”, International Journal of Scientific Research in Multidisciplinary Studies , Vol.3, Issue.6, pp.48-53, 2017.
[16]Rajesh N. “Survey on Privacy Preserving Data Mining Techniques using Recent Algorithms”. International Journal of Computer Applications (0975 – 8887) Volume 133 – No.7, January 2016
[17] Ashaq Hussain Bhat, Puniethaa Prabhu, "OTU Clustering: A window to analyse uncultured microbial world", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.62-68, 2017
[18]Maneesh Upmanyu, Anoop M. Namboodiri, Kannan Srinathan, and C.V. Jawahar, “Efficient Privacy Preserving K-Means Clustering”. Pacific-Asia workshop on intelligence and security informatics ,PAISI 2010 : Intelligence and security informatics pp 154-166
[19]Animesh Tripathy1, Ipsa DE1, “Privacy Preserving Two-Party Hierarchical Clustering Over Vertically Partitioned Dataset” A Journal of Software Engineering and Applications, 2013, 6, 26-31
[20] Jinfei Liu, Li Xiong, Jun Luo, Joshua Zhexue “Privacy Preserving Distributed DBSCAN Clustering”. Transactions of data privacy vol 6 issue 1, april 2013 page 69-85
[21] R. Sasikala T. Bhuvaneswari ,Assistant Professor Department of Computer Science and Engineering ,Sankara College of Commerce and Science

[22] Dr. T. Christopher, “A Study of Clustering Based Algorithm for Outlier Detection in Data streams” Proceedings of the UGC Sponsored National Conference on Advanced Networking and Applications.pp194-197
[23] Kanungo, Tapas, et al. "An efficient k-means clustering algorithm: Analysis and implementation." IEEE transactions on pattern analysis and machine intelligence 24.7 (2002): 881-892.
[24]B.S.Sangeetha,K.N.Nithya,N.Suganya Devi, A.Shyamala Gowri, “An Innovation approach in searching of research paper using text clustering with feature selection” IJMDRR E-ISSN-2395-1885 ISSN -2395-1877
[25] T.Vijaya Kumar, Dr. H.S.Guruprasad “Clustering of Web Usage Data using Hybrid K-means and PACT Algorithms” BIJIT - BVICAM’s International Journal of Information Technology
[26] P.Prabhu “Improving the performance of K-means clustering for high dimensional data sets.” International Journal on Computer Science and Engineering (IJCSE)

Prabhjeet Kaur, Rekha Bhatia, "A Comprehensive Review of Privacy Preservation Framework using Birch and K-Means Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.461-466, 2018.

Green computing in a broader way is the practices and procedures of designing, manufacturing, using of computing resources in an environment friendly way while maintaining overall computing performance and finally disposing in a way that reduces their environmental impact. This means reduction in use of hazardous materials, maximizing output from the product during its lifetime while minimizing energy consumption and also reusability or recyclability and biodegradability of used products and wastes. Many corporate organizations are taking initiatives to reduce the harmful impact of their operations on the environment. United Nations Framework Convention on Climate Change (UNFCC) is an international environment treaty whose objective is to stabilize the emission of green house gases in the atmosphere at a level that would prevent dangerous anthropogenic interference with the eco system. Sustainable development means developing without damaging the requirements of the future generations. That is meeting human development goals while preserving natural resources and ecosystems on which the society depends. This paper is a survey of several important current researches related to the field of green computing which emphasises the importance of green computing for sustainable development.

Sustainable development, Green Computing, Data Centre, Energy efficiency

[1]. Murugesan, San. "Harnessing green IT: Principles and practices." IT professional 10.1 (2008).
[2]. L. Lakhani, "Green Computing - A New Trend in It", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.11-13, 2016
[3]. Soomro, Tariq Rahim, and Muhammad Sarwar. "Green computing: From current to future trends." World Academy of Science, Engineering and Technology 63 (2012): 538-541.
[4]. Usvub, Kafiyah, Abdul Majid Farooqi, and M. Afshar Alam. "Edge Up Green Computing in Cloud Data Centers." International Journal of Advanced Research in Computer Science 8.2 (2017).
[5]. Han, Guangjie, et al. "Resource-utilization-aware energy efficient server consolidation algorithm for green computing in IIOT." Journal of Network and Computer Applications 103 (2018): 205-214.
[6]. Sharma, Manoj Kumar. "Software Level Green Computing with Multi-Core Processors using Fork-and-Join Framework." (2017).
[7]. Kumar, Sushil, Omprakash Kaiwartya, and Abdul Hanan Abdullah. "Green computing for wireless sensor networks: Optimization and Huffman coding approach." Peer-to-Peer Networking and Applications 10.3 (2017): 592-609.
[8]. Farooqi, Abdul Majid. "Comparative Analysis of Green Cloud Computing." International Journal of Advanced Research in Computer Science 8.2 (2017).
[9]. Kharchenko, Vyacheslav, and Oleg Illiashenko. "Concepts of green IT engineering: taxonomy, principles and implementation." Green IT Engineering: Concepts, Models, Complex Systems Architectures. Springer, Cham, 2017. 3-19.
[10]. More, Nitin S., and Rajesh B. Ingle. "Challenges in green computing for energy saving techniques." Emerging Trends & Innovation in ICT (ICEI), 2017 International Conference on. IEEE, 2017.
[11]. Mesaad, Mariam, et al. "Survey on the Global Green Computing Initiatives." International Journal of Computer Applications 167.7 (2017).
[12]. AlMusbahi, Ibtehaj, et al. "Survey on Green Computing: Vision and Challenges." International Journal of Computer Applications 167.10 (2017).
[13]. Kern, Eva. "Green Computing, Green Software, and Its Characteristics: Awareness, Rating, Challenges." From Science to Society. Springer, Cham, 2018. 263-273.
[14]. Tyurin, Sergey, and Anton Kamenskih. "Green logic: models, methods, algorithms." Green IT Engineering: Concepts, Models, Complex Systems Architectures. Springer, Cham, 2017. 69-86.
[15]. Sharma, Prateek, et al. "Design and Operational Analysis of a Green Data Center." IEEE Internet Computing (2017).
[16]. Shaikh, Faisal Karim, Sherali Zeadally, and Ernesto Exposito. "Enabling technologies for green internet of things." IEEE Systems Journal 11.2 (2017): 983-994.
[17]. Pahlevan, Ali, et al. "Joint Computing and Electric Systems Optimization for Green Datacenters." Handbook of Hardware/Software Codesign (2017): 1163-1183.
[18]. Taufiq-Hail, Ghilan Al-Madhagy, Huda Ibrahim, and Shafiz Affendi Mohd Yusof. "Saas Cloud Computing As A Means Of Green It Acceptance Model: A Theory Of Planned Behavior Model At Malaysian Public Universities’context." Journal of Information 2.4 (2017): 01-17.
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Biswajit Saha, "Green Computing: Current Research Trends," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.467-469, 2018.

Sentiment Analysis (SA) is characterized as an intelligent strategy of removing different feelings and feeling of clients. it`s one among the key fields for specialists working in dialect process. The development of net has turned out to be one of the biggest stage for clients to trade their ideas, share messages, post sees and so on. There conjointly exists a few online journals, Google+ that is increasing sensible quality as they enable people to particular their perspectives. amid this paper, the present condition of differed systems of sentiment analysis for feeling mining like machine learning and vocabulary based methodologies square measure specified. the different strategies utilized for Sentiment Analysis is broke down amid this paper to play out an analysis study and check the value of the present writing.

Sentiment, Feature extractions

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Gaganpreet Singh, Rekha Bhatia, "A Survey Sentiment Analysis and Classification Approaches," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.470-475, 2018.

In the past 10-12 years IoT had been deployed in various area of application. For managing countless gadgets and the huge information produced by them, a proficient computing stage is required. Fog computing has been proposed as an answer. It is a worldview stretching out distributed computing and administrations to the edge of the system, in this way decreasing the inertness of dynamic basic leadership and enhancing ongoing execution when all is said in done. This paper gives a view on the flow best in class explore in the region of fog computing and IoT innovation.

Computing system

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Aarti, Supreet Kaur, "A Survey in fog Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.476-479, 2018.

To develop the security, a new image encryption method that uses a keyless approach for image encryption is proposed. Keyless approach increases the ability of the system from various types of attacks as compared to the asymmetric keys that are vulnerable to various types of attacks. For security check of the system hash algorithms (MD5 and SHA128) are applied which checks whether the image transmission over the web is manipulated in between the transfer from sender to the receiver. The image is encrypted in such a way that it is not an easy task for the third party to recognize or create the original image by just seeing the encrypted image. Before the transfer of an image SHA128 and MD5 of the image is calculated. After the decryption of the image if SHA128 and MD5 of the system matches the image is not manipulated otherwise it is manipulated. So the Keyless approach, SHA128 and MD5 together increases the overall security of the system and make the system more secure by protecting it from different types of attacks. The outcome shows that the value of error matrix, PSNR is increased by approximately 8% and the values of MSE is about 31% of the encryption using divergent illumination and asymmetric keys which shows that the result are good in comparison of the encryption using divergent illumination and asymmetric keys.

Optical Image Encryption, Secure Hash Algorithms, Keyless Encryption

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Anchal Monga, Anubhooti Papola, "Keyless Image Encryption using Hash Maps," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.480-484, 2018.