Volume-06 , Special Issue-11 , Dec - 2018 Go Back

Databases provide data storage, extraction and manipulation by using SQL language. It has emerged as a backend to support Big Data applications. It is mainly characterized by horizontal scalability, schema-free data models, and easy cloud deployment. There are various NoSQL databases and the performance varies with different types based on node capacity, number of cores, replication actor, and different workloads. Hence, it is important to compare them in terms of their performance and verify how the performance is related to the different database. This paper focuses on comparison of Cassandra, MongoDB and HBase which are the most commonly used NoSQL databases. This comparison between NoSQL databases deploys them on yahoo cloud platform which uses different types of virtual machines and cluster sizes to study the effect of different configurations. The final result shows the performance of databases at different workload levels and the result can be compared to find out the best among these two databases. In this paper, the comparison of two data bases which are mongo db and Cassandra db algorithm are used to produce the result which is the best db for future data base.

BigData, MongoDB, Cassandra db, Virtual machine

[1] Venkat N Gudivada,Dhana Rao,Vijay V Raghavan,”Nosql systems for Big Data Management “IEEE 2014,DOI 10.1109/SERVICES .2014.42,pp:190-197.
[2] Thomas Sandholm,Dongman Lee,”Notes on Cloud Computing Principles”in Journal of Cloud Computing:Advances,Systems and applications,springer 2014.
[3] Divyakant Agarwal,Sudipto Das,Amr EI Abbadi, ”Bigdata and Cloud Computing:Current State and Future opportunities”,ACM 2011.
[4] Nani Fadzlina Naim, Ahmad Ihsan MohdYassin, Wan Mohd Ameerul Wan Zamri, Suzi Seroja Sarnin, ”Mysql Database for storage of finger print data” IEEE 2011, DOI 10.1109/UKSIM.2011.62,pp:293-298.
[5] Sudhanshu Kulshreshta, Shelly Sachdeva, ”Performance for Data Storage-DB4o and Mysql Databases”, IEEE 2014.
[6] Mehul Nalin Vora, ”Hadoop-HBase for Large Scale Data” , IEEE 2011,pp:601-605.
[7] Gansen Zhao, Weichai Huang, ShunlinLiang, Yong Tang, ”Modelling MongoDB with Relational Model”, IEEE 2013,DOI 10.1109/EIDWT.2013.25,pp:115-121.
[8] Shalini Ramanathan, Savita Goel, Subramanian Alagumlai, ”Comparison of Cloud Database: Amazon‟s SimpleDB and Google‟s BigTable” ,in IEEE 2011 and International Journal of Computer Science Issues(IJCSI), Vol 8,Issue 6,No 2,Nov 2011,ISSN:1694-0814.
[9] Jing Han, Hai Hong E, Guan Le, Jian Du, ”Survey on Nosql Databases” IEEE 2011, pp:363-366.

R. Sasikala, "A Comparitive Study on Mongo and Cassandra Database For Data Clustering", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.147-151, 2018.

Data mining is the process of extracting the concealed information and rules from large databases. But the real world datasets are sparse, dirt and also contain hundreds of items. Frequent Pattern Mining (FPM) is one of the most intensive problems in discovering frequent itemsets from such datasets. Apriori is one of the premier and classical data mining algorithms for finding frequent patterns but it is not an optimized one. So over last two decades a remarkable variations and improvements were made to overcome the inefficiencies of Apriori algorithm such as FPGrowth, TreeProjection, Charm, LCM, Eclat and Direct Hashing and Pruning (DHP), RARM, ASPMS etc., In any case, a little enhancement in the algorithm improves the mining process considerably. Frequent item set mining with vertical data format has been proposed as an improvement over the basic Apriori which reduces the number of database scans and uses array storage structure. This research paper has proposed a space efficient implementation of finding frequent itemsets with vertical data format using jagged array and reduces the usage of memory by allocating exact memory. An experiment is done between the different implementation of vertical data format approaches viz., array, and jagged array representation. From the experiment it is proved that the proposed jagged array implementation method utilizes the memory effectively when compared with the traditional multidimensional array.

Frequent Pattern Mining, Apriori, FPGrowth, Eclat, Vertical Data Format, Array, and Jagged Array

[1]. Liu, Y., Liao, W. K., Choudhary, A. N., & Li, J. (2008). Parallel Data Mining Algorithms for Association Rules and Clustering, In Intl. Conf. on Management of Data, pp.1-25.
[2]. Kumar, G. V., Sreedevi, M., & Kumar, N. P. (2012). Mining Regular Patterns in Data Streams Using Vertical Format. International Journal of Computer Science and Security (IJCSS), 6(2), pp.142-149.
[3]. Ravikiran, D., & Srinivasu, S. V. N. (2016). Regular Pattern Mining on Crime Data Set using Vertical Data Format. International Journal of Computer Applications, 143(13).
[4]. Singla, V. (2016). A Review: Frequent Pattern Mining Techniques in Static and Stream Data Environment. Indian Journal of Science and Technology, 9(45), pp.1-7.
[5]. Ishita, R., & Rathod, A. (2016). Frequent Itemset Mining in Data Mining: A Survey. International Journal of Computer Applications, 139(9).
[6]. Guo, Y. M., & Wang, Z. J. (2010, March). A vertical format algorithm for mining frequent item sets. In Advanced Computer Control (ICACC), 2010 2nd International Conference on (Vol. 4, pp. 11-13). IEEE.
[7]. Han, J., Kamber, M. Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, 2006.
[8]. S.Sharmila, Dr. S.Vijayarani. (2017). Frequent Itemset Mining and Association Rule Generation using Enhanced Apriori and Enhanced Eclat Algorithms, International Journal of Innovative Research in Computer and Communication Engineering, 5(4), pp. 679- 6804.
[9]. Zhiyong Ma, Juncheng Yang, Taixia Zhang and Fan Liu. (2016). An Improved Eclat Algorithm for Mining Association Rules Based on Increased Search Strategy, International Journal of Database Theory and Application, 9(5), pp.251-266.
[10]. C.Ganesh, B.Sathiyabhama and T.Geetha. (2016). Fast Frequent Pattern Mining Using Vertical Data Format for Knowledge Discovery, International Journal of Emerging Research in Management &Technology, 5(5), pp.141-149.
[11]. Hosny M. Ibrahim, M.H. Marghny and Noha M.A. Abdelaziz. (2015). Fast Vertical Mining Using Boolean Algebra, International Journal of Advanced Computer Science and Applications, 6(1), pp.89-96.
[12]. Mohammed J. Zaki amd Karam Gouda. (2003), Fast Vertical Mining Using Diffsets SIGKDD ’03, ACM.

P. Sumathi, S. Murugan, "A Memory Efficient Implementation of Frequent Itemset Mining with Vertical Data Format Approach", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.152-157, 2018.

The colossal volume of data available on the internet media and the evolution of internet and social media, the internet user articulates their opinion, suggestion and views on the web. The comprehensive study of user opinion is quite imperative in the decision making processes. Unearthing the useful content from these opinion sources is a tedious and cumbersome task and this formed a new sector of research field named opinion mining and sentiment analysis. Opinion mining and sentiment analysis automatically extract and classify the user’s opinion clearly. This paper focuses on the review of Opinion mining about a particular topic written in a natural language and classifies them as positive, negative or neutral based on the humans emotions involved in it.

Data source, classification techniques

[1] Walaa Medhat, Ahmed Hassan, Hoda Korashy, Sentiment Analysis: A Survey, Ain shams Engineering Journal, vol.5, pp. 1093-1113, (2014).
[2] Lorraine Chambers, Erik Tromp, Mykola Pechenizki,Mohamed Medhat Gaber “Mobile sentiment analysis”.
[3] Prof. Durgesh M. Sharma, Prof. Moiz M. Baig “Sentiment Analysis on Social Networking: A Literature Review”International Journal on Recent and Innovation Trends in Computing and Communication
[4] Ankita Gupta, et al. “Sentiment analysis of tweets using machine learning approach” International journal of computer science and mobile computing, 2017.
[5] Gayathri Deepthi, K. Sashi Rekha, Opinion Mining and Classification of User Reviews in Social Media, International Journal of Advance Research in Computer Science and Management Studies, vol.2, pp. 37-41, (2014).
[6] Kim Schouten, Onne van der Weijde, Flavius Frasincar, and Rommert Dekker “Supervised and unsupervised aspect category detection for sentiment analysis with co-occurancedata”IEEE transactions on cybernetics.
[7] Gautami Tripathi, Naganna.S, Feature Selection and Classification approach for Sentiment Analysis, An International Journal, vol.2, pp.1-16, (2015).
[8] K. Uma Maheswari, S.P. Raja Mohana, G. Aishwarya Lakshmi, Opinion Mining using Hybrid Methods, International Journal of Computer Applications, pp. 18-21, (2015).
[9] N. Sathyapriya, C. Akila, A Survey on Opinion Mining Techniques and Online Reviews, International Journal of Scientific Development and Research (IJSDR), vol.1, pp. 70-74, (2016).
[10] Richa Sharma, Shweta Nigam, Rekha Jain, Mining of Product Reviews at Aspect Level, International Journal in Foundations of Computer Science & Technology, vol.4, pp. 87-95, (2014)

R.Bhuvaneswari, S. Ravichandran, "A Comprehensive Survey on Sentiment Analysis and Opinion Mining", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.158-161, 2018.

Text summarization is the process of creating short and accurate summary of a given text document. The paper is proposing an abstractive method of text summarization for text in Indian languages. The proposed algorithm uses an encoder-decoder recurrent neural networks with attention mechanism. The results observed were significantly better compared to the performance of already existing Indian language summarizer.

Abstractive Summarization, LSTM, Recurrent Neural Network (RNN)

[1] Y. Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult.IEEE Transactions on Neural Networks, 5(2):157–166, 2017.
[2] K. Cho, B. Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Arxiv preprint arXiv:1406.1078,2017.
[3] M. Johnson et al., “Google’s multilingual neural machine translation system: Enabling zero-shot translation,” Transactions of the Association for Computational Linguistics, vol. 5, no. 20, pp. 339–351, 2016.
[4] J. Lee, K. Cho, and T. Hofmann, “Fully character-level neural machine translation without explicit segmentation,” Transactions of the Association for Computational Linguistics, vol. 5, pp. 365–378, 2017.

Anjali A V, N. Ramasubramanian, A. Santhanavijayan, "Indian Language Text Summarization Using Recurrent Neural Networks", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.162-164, 2018.

The use of Wi-Fi is increasing day-by-day. The use of Wi-Fi enabled phones as android phones, and their communication within a wireless LAN is to be discussed in this paper. This proposed system is in the form of GAN telecommunication network that allows data and voice transmissions within a specific range of interconnected networks. Each mobile device connects to a device which hosts Server which is a part of the network and identifies itself in the routing table. In proposal model allows free calls within the network with a high quality of voice transmission.

WiFi-Wireless fidelity, LAN-Local Area Network, GAN- Generic Access Network

[1]. Shang dan1”A 3G Video phone solution of reducing the call drop”,IEEE computer society .
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[3]. Wikipedia.org/wiki/Voice_over_WLAN.
[4]. Wikipedia,2018,GAN Available at Wikipedia.org/wiki/Generic Access Network_WLAN.
[5]. Darshan Rathi, TomyPallissery , Upendra Bangale”WiFi Call” article at jgrcs volume 5,no.4,2014.
[6]. Sandip Rane, jayasandip, Ritugayakwad, Akankshapatil” Voice calls between Wireless(android) phones and a cooperative application for sending SMS over WiFinetwork” Global journal of computer science. Volume 12 , issue 4.
[7]. "Voice over Internet Protocol. Definition and Overview" International Engineering Consortium. 2007. Retrieved 2009-04-27
[8]. globaljournals.org/GJCST_Volume12/3-Voice-Calls-between-Wireless.

S. Devi, "Learning Based Voice Transmission through Wifi Network", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.165-167, 2018.

Association rule is a data mining techniques which is used to mine several algorithms of applying association rules from the given databases. One of the most important algorithm is Apriori that is used to extract frequent itemsets from large database and association rules are help to discovering the knowledge. Association rules are if/then statements that help uncover relationships between unrelated data in a relational database or other information repository. In this paper we mention different techniques for mining association rules from relational databases and their usage in different areas.

Data mining, Apriori algorithm, Database, Association rules

[1] S.Mihika, N.Sindhu. “A Survey of Data Mining Clustering Algorithms”International Journal of Computer Applications (0975 – 8887) Volume 128 – No.1, October 2015.
[2] K.Jagmeet, M.Neena.“AssociationRuleMining: A Survey” International Journal of Hybrid Information Technology Vol.8, No.7 239-242, 2015.
[3] C. Ila, A. Mari Kirthima. “A Survey on Association Rule Mining Algorithms” International Journal of Mathematics and Computer Research [Volume 1 issue 10, 270-272, 2013.
[4] A. Rajak and M. K. Gupta. “Association Rule Mining:Applications in Various Areas”, International Conference on Data Management.
[5] S.Sonia, Dr. Jyoti. “Multi-Level Association Rule Mining: A Review” International Journal of Computer Trends and Technology (IJCTT) – Volume 6 no.3, 2013.
[6] EvgueniSmirnov“AssociationRule”vhttps://project.dke.maastrichtuniversity.nl/datamining/2013-Slides/lecture-06.ppt.
[7] G.Savi,M.Roopal. “A Survey on Association Rule Mining in Market Basket Analysis” International Journal of Information and Computation Technology. Volume 4, Number 4, pp. 409-414, 2014.
[8] M. Renuka Devi, A. Baby sarojini. “Applications of Association Rule Mining in different databases” Journal of Global Research in Computer science, Volume 3, No. 8, 2012.
[9] M. Donato, Francesca A. Lisi, A. Annalisa, S. Francesco. “Mining Spatial Association Rules in Census Data: A Relational Approach”
[10] M. Abdul Fattah, Mohammed M. Fouad, Philip S.Yu, Tarek F. Gharib “Discovery of Association rules from University admission System data” I.J. Modern Education and Computer Science, 2013, 4, 1-7, 2013.
[11] M.E. Anuradha Bhatia. Computer Engineering, “Big Data Analytics-Apriori Algorithm” http://www.anuradhabhatia.com.
[12] Apriori algorithm” https://www.slideshare.net/INSOFE/ apriori-algorithm-36054672.
[13] J. Omana, S. Monika, B. Deepika. “Survey on efficiency of Association Rule Mining Techniques” International Journal of
Computer Science and Mobile Computing IJCSMC, Vol. 6, Issue. 4, 5–8, 2017.
[14] “Mining Association Rules in large database” http://www1.pu.edu.tw/~ytwang/docs/DM/ Assoc1.ppt.
[15] “Rule based data mining” https://www.tutorialspoint.com/data mining/dm_rbc.htm.
[16] H. Jiawei, K. Michrine, P. Jian. “Data Mining Concepts and Techniques.
[17] “Decisiontreeinduction” https://www.researchgate.net>publication.

M. Sumitha, P.S. Suganya, "A Survey of Mining Association Rule Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.168-173, 2018.

The Healthcare industry is generally “information rich”, but unfortunately not all the data are mined which is required for discovering hidden patterns & effective decision making. Data mining techniques are used to notice knowledge in database and for medical research, mainly in Heart disease prediction. This paper has analyzed prediction system for Heart disease using more number of input attributes. The system uses medical terms such as sex, blood pressure, cholesterol Family history, Smoking , Poor diet , High blood pressure , High blood cholesterol , Obesity , Physical inactivity , Hyper tension etc like 13 attributes to predict the likelihood of patient getting a Heart disease. This research thesis added two more attributes i.e. obesity and smoking. The data mining classification techniques, namely Decision Trees, Naive Bayes, and Support vector machine are analyzed on Heart disease database. The show of these techniques is compared, based on accuracy. As per our results accuracy of Support Vector machine, Decision Trees, and Naive Bayes are 98%,85.5%, and 65.74% respectively. Our analysis shows that out of these three classification models support vector machine predicts Heart disease with highest accuracy.

Data Mining, Naïve Bayes, Support Vector Machine, Decision Tree, Weka Tool

[1] Frawley and G. Piatetsky -Shapiro, Knowledge Discovery in Databases: An Overview. Published by the AAAI Press/ The MIT Press, Menlo Park, C.A 1996.
[2] Yanwei, X.; Wang, J.; Zhao, Z.; Gao, Y., “Combination data mining models with new medical data to predict outcome of coronary heart disease”. Proceedings International Conference on Convergence Information Technology 2007, pp. 868 – 872.
[3] Sellappan Palaniappan, Rafiah Awang, "Intelligent Heart Disease Prediction System Using Data Mining Techniques", IJCSNS International Journal of Computer Science and Network Security, Vol.8 No.8, August 2008
[4] Niti Guru, Anil Dahiya, Navin Rajpal, "Decision Support System for Heart Disease Diagnosis Using Neural Network", Delhi Business Review, Vol. 8, No. 1 (January - June 2007).
[5] Heon Gyu Lee, Ki Yong Noh, Keun Ho Ryu, “Mining Biosignal Data: Coronary Artery Disease Diagnosis using Linear and Nonlinear Features of HRV,” LNAI 4819: Emerging Technologies in Knowledge Discovery and Data Mining, pp. 56-66, May 2007.
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[7] Carlos Ordonez, "Improving Heart Disease Prediction Using Constrained Association Rules," Seminar Presentation at University of Tokyo, 2004.
[8] Kiyong Noh, Heon Gyu Lee, Ho-Sun Shon, Bum Ju Lee, and Keun Ho Ryu, "Associative Classification Approach for Diagnosing Cardiovascular Disease", Springer, Vol:345, pp: 721- 727, 2006.
[9] Franck Le Duff, Cristian Munteanb, Marc Cuggiaa, Philippe Mabob, "Predicting Survival Causes After Out of Hospital Cardiac Arrest using Data Mining Method", Studies in health technology and informatics, Vol. 107, No. Pt 2, pp. 1256-9, 2004.
[10] Latha Parthiban and R.Subramanian, "Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm", International Journal of Biological, Biomedical and Medical Sciences, Vol. 3, No. 3, 2008.
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[13] Blake, C.L., Mertz, and C.J.: “UCI Machine LearningDatabases”, Cleveland heart disease dataset”
[14] E.P. Ephzibah, “A Hybrid Genetic-Fuzzy Expert System for Effective Heart Disease Diagnosis” D.C. Wyld et al. (Eds.): ACITY 2011, CCIS 198, pp. 115–121, 2011. © Springer-Verlag Berlin Heidelberg 2011
[15] Esra Mahsereci Karabulut & Turgay İbrikçi “Effective Diagnosis of Coronary Artery Disease Using The Rotation Forest Ensemble Method” June 2011 / Accepted: 30 August 2011 / Published online: 13 September 2011 # Springer Science+Business Media, LLC 2011
[16] V.V.Jaya Rama krishniah, D.V.Chandra Sekar, Dr.K.Ramchand H Rao, “Predicting the Heart Attack Symptoms using Biomedical Data Mining Techniques” Volume 1, No. 3, May 2012 ISSN – 2278-1080 The International Journal of Computer Science & Applications (TIJCSA)
[17] Han, J., Kamber, M.: “Data Mining Concepts and Techniques”, Morgan Kaufmann Publishers, 2006
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[19] Mrs.G.Subbalakshmi, Mr. K. Ramesh, Mr. M. Chinna Rao, “Decision Support in Heart Disease Prediction System using Naïve Bayes” Indian Journal of omputer Science and Engineering (IJCSE), ISSN: 0976-5166 Vol. 2 No. 2 Apr-May 2011
[20] R. Sumathi, E. Kirubakaran “Enhanced Weighted K-Means Clustering Based Risk Level Prediction for Coronary Heart Disease” European Journal of Scientific Research ISSN 1450- 216X Vol.71 No.4 (2012), pp. 490-500 © Euro Journals Publishing, Inc. 2012
[21] Dr. K. Usha Rani “Analysis of Heart Diseases Dataset Using Neural Network Approach” (IJDKP) Vol.1, No.5, September 2011

D. Bharathi, P. Sundari, "Heart Disease Prediction System Using Data Mining Classification Techniques", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.174-179, 2018.

Cloud Computing is one of the important fields in today’s computer world. Many data are being entered in to the cloud network from time to time. As a result, the traffic in the cloud system is also increased. To reduce the energy many algorithms are used. The Virtual Machine Migration algorithm is an algorithm which helps in reducing the energy to a greater deal. The data passed through the cloud through various users are measured and the time efficiency is found and discussed.

Cloud Computing, Machine Migration, Virtual data

[1] A. Ali-Eldin, J. Tordsson, and E. Elmroth, “An adaptive hybrid elasticity controller for cloud infrastructures,” in Proc. Of Network Operations and Management Symposium (NOMS), 2012, pp. 204–212.
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[3] L. Tom´as and J. Tordsson, “Improving Cloud Infrastructure Utilization through Overbooking,” in Proc. of ACM Cloud and Autonomic Computing Conference (CAC), 2013.
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[5] K. J. A° stro¨m and R. M. Murray, Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2008.

P.S.S. Akilasri, K. Meenakshi, "Reducing Energy Efficiency through Virtual Machine Migration", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.180-183, 2018.

After Cloud Computing a new development of technology is Internet of Things (IOT). The term IOT was coined by Kevin Ashton of Procter & Gamble in 1999.At that point, he viewed Radio-frequency identification (RFID) as essential to the Internet of things at that point which would allow computers to manage all individual things. IOT is a sort of “universal global neural network” in the cloud which connects various things. Other than the communication devices all the physical devices are all going to be connected to the Internet, and should be controlled by the wireless networks. Smart Things is another example in IT world. The Iot concept can be associated with Body Area Networks(BAN), Unmanned Aerial Vehicle networks and Satellite networks. The IoT creates and intellectual invisible network that can be controlled and detected. Future of Internet of Thingswill betransforming the real world objects into intelligent practical objects.

Internet of things – challenges of IoT – Trends of IoT – Applications

[1].https://www.schneier.com/essays/archives/2016/02/the_internet_of_thin_2.html
[2].http://ijesc.org/upload/8e9af2eca2e1119b895544fd60c3b857.Internet%20of%20Things-IOT%20Definition,%20Characteristics,%20Architecture,%20Enabling%20Technologies,%20Application%20&%20Future%20Challenges.pdf
[3].https://ieeexplore.ieee.org/document/7902207/
[4].https://www.sas.com/en_us/insights/big-data/internet-of-things.html
[5].https://internetofthingsagenda.techtarget.com/definition/Internet-of-Things-IoT
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[7]. L. Xu, L. Rongxing, L. Xiaohui, S. Xuemin, C. Jiming, and L. Xiaodong, “Smart community: an internet of things application,” IEEE Communications Magazine, vol. 49, no. 11 pp. 68-75, Nov. 2011.
[8].https://www.analyticsvidhya.com/blog/2016/08/10-youtube-videos-explaining-the-real-world-applications-of-internet-of-things-iot/
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P. Herbert Raj, P. Joseph Charles, M. Merla Agnes Mary, "IoT- New Perspectives and its Security", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.184-188, 2018.

Data mining is the process of analyze data from different perspectives and summarizing it into useful information. Clustering is a practical unsupervised data mining task that subdivides an input data set into a desired number of subgroups so that members will have high similarity and the member of different groups have large differences. K-means is a usually used partitioning based clustering technique that tries to find a user specified number of clusters (k), which are represent by their centroids, by minimizing the square error function. Although K-means is easy and can be used for a wide variety of data types, it is rather sensitive to initial positions of cluster centers. There are 2 simple approach to cluster center initialization i.e. either to select the initial values at random, or to select the first k samples of the data points. Both approach cause the algorithm to converge to sub optimal solutions. Genetic algorithm one of the usually used evolutionary algorithms performs global search to find the solution to a clustering problem. The techniques typically starts with a set of randomly generated individuals called the population and creates successive, latest generations of the population by genetic operations such as natural selection, crossover, and mutation. Each one chromosome of the population represent K no. of centroids. Steps of genetic algorithm are repeatedly applied for a no. of generations to search for suitable cluster centers in the feature space such that a similarity metric of the resultant clusters is optimized. K-means and genetic algorithm based data clustering have been compared in this paper on the basis of their functioning principle, advantage and disadvantage with proper example.

Data mining, K-means, Genetic algorithm

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[3] K.Kameshwaran, K. Malarvizhi, “Survey on Clustering Techniques in Data Mining,” International Journal of Computer Science and Information Technologies, pp.2272-2276, 2014.
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P. Dheivanai, P. Sundari, "A Comparative Study on K-Means and Genetic Algorithm", International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.189-195, 2018.