An Efficient Approach for Supervised Learning Algorithms Using Different Data Mining Tools for Spam Categorization

Spam is the major problem and a big challenge for researcher to reduce spam. Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. This paper shows classification of spam mail and solving various problems is related to web space. This paper also shows measures parameter which are helpful for reduce the spam or junk mail. Many machine learning algorithm are using to classified the spam and legitimate mail. This paper proposes the best classifier and better classification approach using different data mining tools using bench mark dataset. The dataset consist of 9324 records and 500 attributes used for (training and testing) to build the model. In this paper, a procedure that can help eliminate unsolicited commercial e-mail, viruses, Trojans, and worms, as well as frauds perpetrated electronically and other undesired and troublesome e-mail. This paper shows analyzing of different supervised classifiers technique using different data mining tools such as Weka, Rapid Miner, and Support Vector Machine. This paper shows Weka data mining tool give highest accuracy over different data mining tools.