Riya Qureshi,â€ Cloud Centric loT Based Farmerâ€™s Virtual Market placeâ€ International Journal of Computer Engineering In Research Trends., vol.3, no.12, pp. and Yang,Q., â€œA support vector machine based naive Bayes algorithm for spam filtering,â€ 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC), Las Vegas, NV, 2016, pp. â€œThe Effect of Best First and Spreadsubsample on Selection of a Feature Wrapper With NaÃ¯ve Bayes Classifier for The Classification of the Ratio of Inpatientsâ€. ML & KD- Machine Learning & Knowledge Discovery Group. S., â€œDetecting Threat E-mails using Bayesian Approachâ€, IJSDIA International Journal of Secure Digital Information Age, Vol. Han kwai, â€œApproximate distributed k-means clustering over a peer-to-peer networkâ€, IEEE transactions on knowledge and data engineering, 2009. Boomjia M.D, â€œComparison of partitioning based clustering algorithmsâ€. Shah Sourabh, Singh Manmohan, â€œcomparison of a time efficient modified k-mean algorithm with k-mean and kmedoid algorithmâ€ international conference on communication systems and network technologies, 2012. Na shi, â€œResearch on k-means clustering algorithmâ€, 3rd international symposium on intelligent information technology and security informatics, 2011. Learning to filter unsolicited commercial e-mailâ€, Technical report NCSR Demokritos, 2011. Androutsopoulos, I., Paliouras, G., and Michelakis, â€œE. and Robet, M, â€œClassification spam emails using text and readability featuresâ€, IEEE 13th International Conference on Data Mining, 2013. G.Kaur, R.K.Gurm, â€œA Survey on Classification Techniques in Internet Environmentâ€, International Journal of Advance Research in Computer and Communication Engineering, vol. L.Firte, C.Lemnaru, R.Potolea, â€œSpam Detection Filter using KNN Algorithm and Resamplingâ€, 6th International Conference on Intelligent Computer Communication and Processing- IEEE, 2010, pp.27-33. P.Verma, D.Kumar, â€œAssociation Rule Mining Algorithmâ€™s Variant Analysisâ€, International Journal of Computer Application (IJCA), vol. Marek Rychly, Pavlina Ticha, â€œA tool for clustering in data miningâ€, International Federation for Information Processing, 2007. Furthermore, we also proposed NaÃ¯ve Bayes Algorithm for Email Spam Filtering on SPAMBASE Dataset. Iterations leads the easy cluster formation in this way the efficiency of the traditional K-means clustering algorithm is

The advantage of separating more massive datasets is that handle easy, and the benefit of reducing It will inherit the some of the features from two revised K-meansĪlgorithms. Here we will propose a Hybrid K-Means clustering algorithm which will mostly work on the concept of splittingĭataset and reducing the number of iterations. Regarding Number of iterations are more due to clusters details not known at an initial stage. It is the fast, simple and can work with large datasets. K-means clustering algorithm is one of the most widely used partitioning algorithms used for grouping theĮlements over spatiotemporal data.

An Effective algorithm for Spam Filtering and Cluster FormationĪssociate Professor, Department of Computer Science and Engineering.

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