Heterogeneous clustering

Web pages now-a-days have different forms and types of content. When the web content is considered they are in the form of pictures, video, audio files and text files in different languages. The present study is aimed at this. The content can be multilingual and heterogeneous. The content of the web is considered as images. Statistical features of the images are extracted. The extracted features are presented to the FCM and subtractive clustering, with similarity metric being Euclidean distance. The accuracy is compared.