Web pages nowadays have different forms and types of content. When the Web content is considered, they are in the form of pictures, videos, audio files, and text files in different languages. The content can be multilingual, heterogeneous, and unstructured. The mining should be independent of the language and software. Statistical features of the images are extracted from the pixel map of the image. The extracted features are presented to the fuzzy clustering algorithm (FCM) and Gath–Geva algorithm. The similarity metric being Euclidean distance and Gaussian distance, respectively. The accuracy is compared and presented. © Springer India 2015.

Anand Rajaraman

Artificial Intelligence

Evolutionary algorithms

Fuzzy clustering

Social networking (online)

AUDIO FILES

CONTENT MINING

Euclidean distance

Similarity metrics

STATISTICAL FEATURES

TEXT FILE

WEB CONTENT

WEB MINING

Clustering algorithms

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Clustering for Knowledgeable Web Mining

Advances in Intelligent Systems and Computing

Internet and mobile computing have become a major societal force in that down-to-earth problems and issues are being addressed and sorted out. For this to be effective, information and content extraction need to be at a basic generic level to address different sources and types of web documents and preferably not dependent on any major software. The present study is a development in this direction and focuses on extraction of information from the available text and media-type data as it is stored in the computer, in digital form. The approach is based on operating generic pixel-maps-as stored for any data-so that issues of language, text-script and format do not pose problems. With the pixel-maps, as bases, different methods are used to convert into a numerical form for suitable neural modeling and content is extracted with ease so that approach is universal. Statistical features of the pixel-maps are extracted from the pixel map matrix of the image. The extracted features are presented to neural model and standard Back Propagation algorithm with hidden layers is used to extract content. The accuracy is compared to give the validity of the approach as to how the content extraction within certain bounds could be possible for any web page. © 2015 IEEE.

Proceedings - 2015 2nd International Conference on Soft Computing and Machine Intelligence, ISCMI 2015

Neural Modeling and Content Extraction

Internet and mobile computing have become a major societal force in that down-to-earth issues are being addressed and sorted out whether they relate to online shopping or securing driving information in unknown places. Here the major concern of communication is that the Web content should reach the user in a short period of time. So information extraction needs to be at a basic level and easier to implement without depending on any major software. The present study focuses on extraction of information from the available text and media-type data after it is converted into digital form. The approach uses the basic pixel map representation of data and converting them through numerical means, so that issues of language, text script and format do not pose problems. With the numerically converted data, key clusters similar to keywords used in any search method are developed and content is extracted through different approaches making it computation-intensive for easiness. One approach is that statistical features of the images are extracted from the pixel map of the image. The extracted features are presented to the fuzzy clustering algorithm. The similarity metric being Euclidean distance and the accuracy is compared and presented. The concept of ambiguity is introduced in the paper, by comparing objects like ‘computer,’ which have explicit content representation possible to an abstract subject like ‘soft-computing,’ where vagueness and ambiguity are possible in representation. With this as the objective, the approach used for content extraction is compared and how within certain bounds it could be possible to extract the content. © Springer India 2016.

Mining ambiguities using Pixel-Based content extraction

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. © 2014 IEEE.

2014 International Conference on Information Communication and Embedded Systems, ICICES 2014

Heterogeneous clustering

Communication through web is becoming increasingly popular thanks to wireless and cellular networks. As this awareness spreads far and wide in different countries, significant complexities arise in terms of language and communication means for extracting information on the web. This is particularly true in India where more than fifteen officially recognized language texts and more variations in local dialect exist. An example is in Tamilnadu where Tamizh, native language with its own variations like Chennai, Madurai and Coimbatore dialects is combined effectively and easily with other languages Telugu, Kannada and Malayalam from nearby states and English and Hindi from global and national perspectives. So a web document here could be in any one of the languages or a mixture of words from different languages to avoid translation like 'computer' of English doesn't have translation in Tamizh. There are several aspects to this variational usage with language protagonists and communication engineers. But the complexity in the web document due to these variations does create difficulties in using conventional data mining approaches. The present study focuses attention on this, beginning from text variations to word and document. Typical characters which have similar usage like 'a' in English with those in Tamizh and Telugu are taken and their pixelmaps are mapped for similarity and contrasts. This is later extended to more complex characters like unknown sign in Telugu which is one character as compared to its English equivalent 'kO' making representations difficult. When one starts looking at words, complexity increases as 'temple' in English translated as 'unknown sign' in Telugu or 'mandiram' written in English. Similarities in pixel-maps are looked at and characteristics in terms of matrices are projected so that mining content when such words or letters are extracted in web document can be put in a probabilistic format with predictions based on correlations. Typical histograms highlighting these aspects are presented and later an experiment with a document page dealing with magnetism is used as model-l for predicting content. ©2010 IEEE.

Journal	Data powered by TypesetAdvances in Intelligent Systems and Computing
Publisher	Data powered by TypesetSpringer Verlag
ISSN	21945357
Open Access	No