A Novel Stream Clustering Framework for Spam Detection in Twitter
The project is to classify spam tweets based on twitter account and content based features on twitter handles.
DenStream was promoted by the proposed framework, called here as INB-DenStream. To show the effectiveness of INB-DenStream, state-of-the-art methods such as DenStream, StreamKM++, and CluStream were applied to the Twitter datasets and their performance was determined in terms of purity, general precision, general recall, F1 measure, parameter sensitivity, and computational complexity. The compared results implied the superiority of our method to the rivals in almost the datasets.
An innovative stream clustering framework is introduced as a versatile approach, which is able to enhance the performance of all stream clustering methods. This framework enhances the online phase of stream clustering methods, by replacing the distance function with a set of incremental classifiers in order to more precisely assign incoming samples to the most related microclusters.