BibTeX:

@article{IJIRSTV2I3044,
     title={Clustered K-Nearest Neighbor Process for Spam Detection in Social Networks},
     author={Jyotika Verma and Dr. Sanjeev Dhawan},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={2},
     number={3},
     pages={90--93},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV2I3044.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Spam is one of the issues of the Internet which required attention the most especially on the social networking websites and that’s why spam detection is a very concerned issue. Although there are various issues related to the Internet but spam is the one which is faced by everyone who is using the Internet. There are various strategies which are used by the researchers to detect the spam; this is discussed in the Literature Review section of this paper. KNN is one of those techniques and it is abbreviated as K-Nearest Neighbor Classification, it is basically used to find the nearest neighbor based on the pattern recognition and it is a non-parametric technique. Here, in this research paper KNN is used with the Clustering process because of which the process is time efficient and that too with great accuracy. The data which has been provided for the entire process to be performed is extracted from the social networking website Twitter with the help of R package as it provides interface with the Twitter web API (Application Programmable Interface). Various Calculations has been performed to calculate the accuracy, precision, and other parameters and based on these results and respective graphs have been obtained.

Clustering, KNN, Preprocessing, R package, Social Networks, Spam, Stop word removal