caa a22 4500 605470855 CHVBK 20210128100330.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00500-014-1479-2 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1479-2 Enhancement of spam detection mechanism based on hybrid $$\varvec{k}$$ k -mean clustering and support vector machine [Elektronische Daten] [Nadir Elssied, Othman Ibrahim, Ahmed Osman] Spam e-mails are considered a serious violation of privacy. It has become costly and unwanted communication. Support vector machine (SVM) has been widely used in e-mail spam classification, yet the problem of dealing with huge amounts of data results in low accuracy and time consumption as many researches have demonstrated. This paper proposes a hybrid approach for e-mail spam classification based on the SVM and $$k$$ k -mean clustering. The experiment of the proposed approach was carried out using spambase standard dataset to evaluate the feasibility of the proposed method. The result of this combination led to improve SVM and accordingly increase the accuracy of spam classification. The accuracy based on SVM algorithm is 96.30% and the proposed hybrid SVM algorithm with $$k$$ k -mean clustering is 98.01%. In addition, experimental results on spambase datasets showed that improved SVM (ESVM) significantly outperforms SVM and many other recent spam classification methods. Springer-Verlag Berlin Heidelberg, 2014 $$k$$ k -mean clustering nationallicence Mechanism nationallicence Non-spam nationallicence Spam detection nationallicence SVM nationallicence Spam nationallicence $$t$$ t test nationallicence Coefficient correlation nationallicence Elssied Nadir Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia aut Ibrahim Othman Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia aut Osman Ahmed Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia aut Soft Computing Springer Berlin Heidelberg 19/11(2015-11-01), 3237-3248 1432-7643 19:11<3237 2015 19 500 https://doi.org/10.1007/s00500-014-1479-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1479-2 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Elssied Nadir Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia aut NATIONALLICENCE 700 1- Ibrahim Othman Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia aut NATIONALLICENCE 700 1- Osman Ahmed Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/11(2015-11-01), 3237-3248 1432-7643 19:11<3237 2015 19 500