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Clustering is the process of combination of identical objects into same classes. A cluster is a grouping of data objects that are analogous to one another within the same cluster and are disparate to the objects in other clusters. Data clustering can be performed on various areas such as data mining, statistics, machine learning, spatial database, biology and marketing. Machine learning is classified into supervised and unsupervised learning. Clustering is the example of unsupervised learning that has no predefined classes and deals with unknown samples. Cluster analysis can be done with different types of methods includes partitioning methods, hierarchical methods, density based methods, grid based methods and model based methods. Quality of clusters can be determined by the two factors that they are high intra-cluster similarity and low inter-cluster similarity. In this paper, various clustering techniques has been analyzed in data mining in terms of methodology adopted, dataset handled, accuracy, advantages and limitations.

Agglomative approach, Clustering, K-means, K-medoid

[1] Pushpalatha K.P., and Raju G., “Text Document Retrieval through Clustering using Meaningful Frequent Ordered Word Patterns", In: International Journal of Applied Engineering Research, Volume.13, pp. 4822-4833, ISSN: 0973-4562, 2018.
[2] Hemanth Somasekar and Kavya Naveen, “Text Categorization and graphical representation using Improved Markov Clustering", In: International Journal of Intelligent Engineering and Systems,Volume.11, pp.107-116, March 2018.
[3] Dr.Vo Ngoc Phu and Dr.Vo Thi Ngoc Tran, “English Sentiment Classification Using an YULEQ Similarity Measure and the One dimensional Vectors in a Parallel Network Environment”, In: Journal of Theoretical and Applied Information Technology, Volume.96, pp.3356-3382, ISSN: 1992-8645, June 2018.
[4] Arpit Bansal, Mayur Sharma, Shalini Goel, “Improved K-mean Clustering Algorithm for Prediction Analysis using Classification Technique in Data Mining”, In: International Journal of Computer Applications, Volume.157, pp.35- 40, January 2017.
[5] Vairaprakash Gurusamy S., Kannan, J. and Regan Prabhu, “Mining the Attitude of Social Network Users using K-means”, In: International Journal of Advanced Research in Computer Science and Software Engineering, Volume.7, Issue.5, pp.226-230, ISSN: 2277-128X, May 2017.
[6] Suman, Pinki Rani, “A Survey on STING and CLIQUE Grid Based Clustering Methods”, In: International Journal of Advanced Research in Computer Science, Volume.8, pp.1510-1512, ISSN: 0976-5697, May-June 2017.
[7] Chitra K., and Maheswari D., “A Comparative Study of Various Clustering Algorithms in Data Mining”, In: International Journal of Computer Science and Mobile Computing, Volume.6, Issue.8, pp.109-115, ISSN: 2320–088X, August 2017.
[8] Sajana T., Sheela Rani C.M., and Narayana K.V, “A Survey on Clustering Techniques for Big Data Mining”, In: Indian Journal of Science and Technology, Volume.9, pp.1-12, ISSN: 0974-6846, January 2016.
[9] Shilna S. and Navya EK, “Heart Disease Forecasting System using K-mean Clustering Algorithm with PSO and other Data Mining Methods”, In: International Journal On Engineering Technology and Sciences, Volume.3, Issue.4, pp.51-55, ISSN(P): 2349-3968, ISSN (O): 2349-3976, April 2016.
[10] Kiranjeet Kaur and Lalit Mann Singh, “Heart Disease Prediction System Using ANOVA, PCA And SVM Classification”, In: International Journal of Advance Research, Ideas and Innovations in Technology, Volume.2, Issue.3, pp.1-6, ISSN: 2454-132X, 2016.
[11] Swarndeep Saket J. and Sharnil Pandya, “An Overview of Partitioning Algorithms in Clustering Techniques”, In: International Journal of Advanced Research in Computer Engineering & Technology, Volume.5, Issue.6, pp.1943-1946, ISSN: 2278–1323, June 2016.
[12] Abdullah Z. and Hamdan A. R, “Hierarchical Clustering Algorithms in Data Mining”, In: International Journal of Computer and Information Engineering, Volume.9, pp.2201- 2206, 2015.
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[14] Abdullah Z, Hamdan A. R, “Hierarchical Clustering Algorithms in Data Mining”, In: International Journal of Computer and Information Engineering, Volume.9, pp.2201- 2206, 2015.
[15] Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann, Third Edition, 2012.
[16] Mohammed M. Abu Tair, Alaa M. El-Halees, “Mining Educational Data to Improve Students’ Performance: A Case Study”, In: International Journal of Information and Communication Technology Research, Volume.2, pp.140-146, February 2012.
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[18] M. Kasthuri, Dr. S. Britto Ramesh Kumar, “PLIS: Proposed Language Independent Stemmer for Information Retrieval Systems Using Dynamic Programming”, In: 2016 World Congress on Computing and Communication Technologies, ISBN: 978-1-5090-5573-9, pp.132-135, IEEE, 2016.
[19] M. Kasthuri and Dr. S. Britto Ramesh Kumar, “Multilingual Phonetic Based Stem Generation”, In: Proceedings of the Second International Conference on Emerging Research in Computing, Information Communication and Applications (ERCICA-2014), ELSEVIER Science :: Technology India, ISBN:9789351072 607, Volume.1, pp.437-442, 01-02 August, 2014.
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M. Kasthuri, S. Kanchana, R. Hemalatha, "A Comparative Study on Various Clustering Techniques in Data Mining", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.1-8, 2018.

In mining frequent Itemsets, Eclat algorithm is an important one. But it has some inefficiency. We proposed an algorithm called Improved_Eclat which is a new improved eclat method with high efficiency in the searching process to reduce the running time using two dimensional pattern tree. By comparing Improved_Eclat with Eclat , Eclat-opt and Bi-Eclat, hereby it is proved that the Improved_Eclat has the highest efficiency in mining associating rules from various databases.

Association rules, Eclat, increased search approach, increased two- dimensional pattern trees

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V. Priya, S.Murugan, "A Performance Analysis of Improved_Eclat Algorithm in Association Rule Mining", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.9-13, 2018.

Heart disease is one of the major causes of death in human life. But, prediction of heart disease with desired accuracy is difficult due to many reasons. For example, the database of heart disease is being archived for many years with huge volume which are too large for traditional systems to process. Also, the clinical reports and medical tests related to heart disease produce in a variety of formats such as text, images, sound etc which are not effectively handled by traditional database systems. Nowadays data mining algorithms and big data technologies play crucial role in the prediction of heart diseases. In addition, big data techniques are useful in finding the patterns of heart disease in its early stage. Analysis of heart disease data can be done on big scale using Hadoop, R and MapReduce. In our previous paper, we proposed a conceptual approach for prediction of heart diseases with Support Vector Machine (SVM) in parallel programming fashion. In relation to our previous work, in this paper, an investigation is done in finding the applicability of different big data tools and technologies for prediction of heart diseases.

Big data tools, Hadoop, Map Reduce, prediction of diseases

[1]. Revathi.T, Jeevitha,”Comparative Study on Heart Disease Prediction System Using Data Mining Techniques”,International Journal of Scienc and Research,Volume:4,Issue 7,July 2015,PP.2120-2123.
[2]. Miss.Chaitrali S.Dangare, Dr.Mrs.Sulabha S.Apte,”A Data Mining Approach for Prediction of Heart Disease Using Neural Networks”,International Journal of Computer Engineering & Technology,Volume:3,Issue 3,December 2012,pp.30-40
[3]. Thenmozhi.K, Deepika.P,” Heart Disease Prediction Using Classification with Different Decision Tree Techniques”, International Journal of Engineering Research and General Science,Volume:2,Issue 6,November 2014,pp.6-11.
[4]. Subha.V, Revathi.M, Murugan.D,“Comparative Analysis of Support Vector Machine Ensembles for HeartDiseasePrediction”,InternatinalJournalofComputerScience&CommunicationNetworks,Volume:5(6),December 2015,pp.386-390
[5]. Deepali Aggarwal, ”Differences between traditional data and big data”,June30,2016
[6]. Andrea Di Lenarda et al, ”The future of telemedicine for the management of heart failure patients ”,Journal of the European Society of Cardiology, May 2017
[7]. Sharmila.R, Chellammal.S,”A conceptual method to enhance the prediction of heart disease using big data techniques”, International Journal of Computer Science and Engineering,Volume:6,Issue 4,May 2018,pp.25.

R. Sharmila, S. Chellammal, "Application of Big Data Tools and Techniques in Prediction of Heart Diseases", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.14-16, 2018.

In this research, a cross-layer optimized energy-aware multipath routing protocol (EMRP) for mobile ad hoc networks (MANET) is proposed. By sharing the information among the physical layer, the MAC sub-layer and the network layer, EMRP efficiently utilizes the network resources such as the node energy and the link bandwidth. Simulation results show that the protocol prolongs the network lifetime, increases the volume of packets delivered, lowers the energy dissipation per bit of data delivery and shortens the end-to-end delay. The growth of interest and research on multihop wireless network is exponential in recent years. In mobile ad hoc networks (MANET), the nodes play the role of routers to forward the packets of neighbor nodes as there is no fixed infrastructure available to do so. Network is a proven solution that maps the architecture of cellular networks into ad hoc networks. Here, selected nodes form the virtual backbone of the network and take part in packet routing. This achieves faster packet delivery as limited nodes are responsible for the same even though the network is not strongly connected. In this paper, a distributed topology adaptive clustering algorithm is designed that requires local information by the nodes for the formation of clusters. The role of cluster head is fairly distributed among the nodes to obtain a longer network lifetime. The change of cluster heads and the mobility of nodes disturb the node connectivity resulting in communication instability. To overcome such situations, a topology control protocol is developed that adjusts the transmission range of concerned mobile nodes to achieve local connectivity among nodes within the clusters even after the hand-off by the heads takes place.

EMRP, MANET, MAC sub-layer, network layer, node energy, link bandwidth

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[6] A. Tsirigos and Z.J. Haas, "Multipath routing in mobile ad hoc networks or how to route in the presence of frequent topology changes," In Proceedings of IEEE MILCOM 2001, McLean, VA, Oct. 2001, pp. 878- 883.
[7] A. Tsirigos and Z.J. Haas, "Multipath routing in the presence of frequent topological changes," IEEE Communications Magazine, vol. 39, no. 11, Nov. 2001, pp. 132 -138.
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S.J. Sangeetha, T. Rajendran, "The Node Energy of Multipath Routing Protocol for Mobile Ad Hoc Networks", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.17-22, 2018.

Visual Cryptography is a new cryptographic technique which allows visual information (pictures, text, etc.) to be encrypted in such some way that the decipherment will be performed by human, with none decipherment algorithmic rule. Here we have a tendency to propose a knowledge activity in halftone pictures victimisation conjugate ordered video digitizing (DHCOD) algorithmic rule that could be a changed version of information activity in halftone pictures victimisation conjugate error diffusion technique (DHCED). We have a tendency to use this DHOCD algorithmic rule for proposing a brand new 3 part visual cryptography theme. DHCOD technique is employed to cover Associate in Nursing binary visual pattern in 2 or additional ordered dither halftone pictures, which may be from identical or completely different multi-tone pictures. In projected theme we have a tendency to shall generate the shares victimisation basic visual cryptography model so imbed them into a canopy image employing a DHCOD technique, so the shares are going to be safer and pregnant.

Secret shares, Halftone pictures, Visual cryptography, VCS, Watermarking, DHCED and DHCOD

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S. Ponnarasi, T. Rajendran, "DHCED and DHCOD Technique of Visual Cryptography Scheme for Encrypted and Decryption", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.23-27, 2018.

Security and protection issues are amplified by the volume, assortment, and speed of Big Data. The decent variety of information sources, arrangements, and information streams, joinExternalize data security when possible and d with the gushing idea of information procurement and high volume make one of kind security dangers. This paper points of interest the security challenges when associations begin moving touchy information to a Big Data store like Hadoop. It distinguishes the diverse danger models and the security control structure to address and alleviate security hazards because of the recognized risk conditions and use models. The system laid out in this paper is likewise intended to be circulation skeptic.

Hadoop, Big Data, enterprise, defense, risk, Big Data Reference Framework, Security and Privacy, threat model

[1] EMC Big Data 2020 Projects http:// www.emc.com/leadership/digitaluniverse/iview /big-data-2020.html
[2] NIST Special Publication 1500-1 NIST Big Data Interoperability Framework: Volume 1, Definitions http:// bigdatawg.nist.gov/_uploadfiles/M0392_v1_3022325181.pdf
[3] Securosis – Securing Big Data Security issues with Hadoop environments https://securosis.com/blog /securing-big-datasecurity-issues-with-hadoop-environments
[4] Top 10 Big Data Security and Privacy Challenges, Cloud Security Alliance,2012 https://downloads .cloudsecurityalliance.org/inititives/bdwg/ Big_Data_Top_Ten_v1.pdf
[5] Hadoop in Action, Second Edition by Manning Publications.ISBN:9781617291227http://www.manning. com/ lam2/

S. Regha, M. Manimekalai, "Anchoring Your Big Data Environment", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.28-34, 2018.

Social media is a growing trend in the world today. It is being utilized by students, parents, businesses and religious organizations. Nowadays mostly every human being becomes addicted to social media, i.e. Facebook, Twitter and WhatsApp. Usages of social media are increasing in trends. They can build a personal network of friends that is connected to an open worldwide community. Information is now shared freely between the two. These parties can communicate either publicly or via the more discrete personal message. In this paper contains Facebook, Twitter and WhatsApp dataset like status and profile photo. The goal here is to analyze the time execution, Execution process and frequency by implementing weka tool. Here analogize the three algorithms, namely K-means, Bayesion algorithm and apriori algorithm. In this research process, the three algorithms used to find the time execution, Execution process and frequency which are predicting time consumes.

Facebook, Twitter, WhatsApp, Bayesion algorithm, K- Means algorithm, Apriori algorithm

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[2]. G.ThirumaniAatthi, R.Aishwarya, R.Mallika, and Angel “PREDICTION OF SOCIAL NETWORK SITES USING WEKA TOOL”International journal of advanced technology and science, volume 3, Isssue 1, Nov-2015. ISSN-2348.
[3]. Andrzejewski, David, XiaojinZhu, Mark Craven, and Ben Recht [3]“Learning from Bullying Traces in Social Media” IJCAI, pages 1171–1177, 2011.
[4]. Boyd, D., & Ellison, N., (2007), ‘Social network sites: Definition, history, and scholarship’, Journal of Computer-Mediated Communication, 13(1),.Retrieved August 22, 2009 from http://jcmc.indiana.edu/vol13/issue1/boyd.ellison.htm , pp.1-11
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M. Saranya kala , "A Comparative Study of Social Media Data Using Weka Tool", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.35-39, 2018.

Information is the backbone of any Organization. To succeed in this situation, one must manage the information in the right amount and at right time. Data Mining is used to mine the new pattern or trends or rules from the unknown/ large amount of data / unpredictable data sets. Data Mining is used at diverse fields like Educational, Agriculture, Medical, Police Department, Research side, Information Technology side and Image processing etc., This Paper analyzes the mindset of Final year UG student about PG admission. For that 18 attributes and one class label collected from the final year UG students then applied on the Data Mining Tools like Weka Tool and Orange Tool. The data sets passed on the classification algorithms like J48, Naive Bayes, RandomForest and REPT Tree in Weka and Classification Tree, CN2, Naive Bayes and kNN of Orange Data Mining Tool. The Confusion matrix, Training and simulated Errors and Testing and Validation Results are obtained and tabulated. The Weka and Orange data mining tools classifiers performance are represented in the graphical form and its decision tree. From the decision tree, hidden rules are extracted, from which possible to determine factors which affects the PG admission. From the Weka tool, obtain attributes interest, jobavailability, feestat, colinfra and scholarship can predict the PG admission. In orange data mining tool, attributes interest to study, jobavailability, feestatus, scholarship, gender, pregovexam and colinfra from the original data set can predict the PG admission.

Data Mining, Weka, Orange Data Mining Tool, Classification Algorithms, Evaluation Result, Confusion Matrix, Rules.

[1] Rakesh Kumar Arora, Department of Computer Science, Krishna Engineering College, Ghaziabad, UP, India, Dr. Dharmendra Badal, Dept. of Mathematical Science & Computer Applications, Bundelkhand University, Jhansi, U.P, India, ”Admission Management through Data Mining using WEKA”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, October 2013.
[2] Neeraj Bhargava, MDS University, Anil Rajput, Govt, PG Nodal College, Sehore (M.P) India, Pooja Shrivastava, Research Scholar Barkatullah University, Bhopal, “Mining higher educational students data to analyze student’s admission in various discipline”, Binary Journal of Data Mining & Networking 1 (2010) 01-05.

P. Sundari, "Analysis of PG Admission in Arts and Science College using Data Mining Tools", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.40-43, 2018.

Heart disease is predicted by classification technique. The data mining tool WEKA has been utilized for implementing J48 classifier. Proposed work is framed with a specific end goal to enhance the execution of models. For improving the classification accuracy J48 is combined with Bagging and Feature Selection. Trial results demonstrated a critical change over in the current J48 classifier. This approach enhances the classification accuracy and reduces computational time.

Data mining, Heart diseases, WEKA, classification, J48, Bagging

[1] KanikaPahwa, Ravinder Kumar,” Prediction of Heart Disease Using Hybrid Technique For Selecting Features”, 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON) GLA University, Mathura, Oct 26-28, 2017.
[2] Salha M. Alzahani, AfnanAlthopity, AshwagAlghamdi, BoushraAlshehri, and SuheerAljuaid “An Overview of Data Mining Techniques Applied for Heart Disease Diagnosis and Prediction”, Lecture Notes on Information Theory Vol. 2, No. 4, December 2014.
[3] Jay Gholap” Performance Tuning Of J48 Algorithm For Prediction Of Soil Fertility”,.
[4] ShahedAnzarusSabab, Ahmed IqbalPritom, Md. AhadurRahmanMunshi, Shihabuzzaman” Cardiovascular Disease Prognosis Using Effective Classification and Feature Selection Technique”,
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R. UmaDevi, Raynuka Azhakarsamy , J.G.R. Sathiaseelan, "A Novel Approach for Heart Disease Classification using Feature Selection", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.44-48, 2018.

Cloud computing is an environment that enables its users to store data in virtualized storage. Cloud provides different services to users; the prime aim of this service usage is to store data in the cloud. When the users store data in the cloud, security of the data is becoming the topmost challenge to be considered in the cloud environment. There are different approaches and techniques which are proposed to address the security of data in the cloud. This paper presents a study and review of different security algorithms that are used in the cloud to secure the cloud environment. The paper presents the comparison of security algorithms with respect to different parameters. Findings and observations obtained from the study of different security algorithms are discussed. Finally, the paper suggests some points to be considered at the time of developing security algorithms for the cloud environment.

Cloud computing; security; security algorithms; cryptography techniques

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A. Ahadha Parveen, P.S.S Akilashri, "Analysis of Security Algorithms used to secure Cloud Environment", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.49-54, 2018.