Spam Filtering using K mean Clustering with Local Feature Selection Classifier
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10.5120/18951-0096 |
Anand Sharma and Vedant Rastogi. Article: Spam Filtering using K mean Clustering with Local Feature Selection Classifier. International Journal of Computer Applications 108(10):35-39, December 2014. Full text available. BibTeX
@article{key:article,
author = {Anand Sharma and Vedant Rastogi},
title = {Article: Spam Filtering using K mean Clustering with Local Feature Selection Classifier},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {108},
number = {10},
pages = {35-39},
month = {December},
note = {Full text available}
}
Abstract
In this paper, we present a comprehensive review of recent developments in the application of machine learning algorithms to Spam filtering, focusing on textual approaches. We are trying to introduce various spam filtering methods from Naïve Bias to Hybrid methods for spam filtering, we are also introducing types of filters recently used for spam filtering along with architecture of spam filter and its types . In this paper we are proposing a technique using Local feature classification methods with K mean clustering algorithm in classifier, for spam filtering term selection we are using Document frequency method, for feature extraction we are using bag of words model for classification we are using k-mean clustering method along with local concentration based extraction of content. This method gives good results along with all parameters.
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