Deepa .C

Work place: Department of Information Technology, SNR Sons College, Coimbatore- 641 006, India

E-mail: deepa_pkd@rediffmail.com

Website:

Research Interests: Data Mining, Data Compression, Data Structures and Algorithms

Biography

Ms. C Deepa received her Masters Degree in Computer Applications from Bharathiar University, Coimbatore, Tamil Nadu, India in the year 2000 and received M.Phil degree in Computer Science from Bharathiar University, Coimbatore, Tamil Nadu, India in the year 2004. Currently she is working as Assistant Professor, Department of Information Technology, SNR SONS College, Coimbatore, TamilNadu . She has more than 12 years of experience in teaching. She is doing Ph.D in Computer science at SNR Sons College, Coimbatore, under the supervision of Dr. Anna Saro Vijendran, Director & Head, Department of Computer Applications, S.N.R Sons College. Her research interests include Data Mining ,Web Mining and OOPS.

Author Articles
SANB-SEB Clustering: A Hybrid Ontology Based Image and Webpage Retrieval for Knowledge Extraction

By Anna Saro Vijendran Deepa .C

DOI: https://doi.org/10.5815/ijitcs.2015.01.05, Pub. Date: 8 Dec. 2014

Data mining is a hype-word and its major goal is to extract the information from the dataset and convert it into readable format. Web mining is one of the applications of data mining which helps to extract the web page. Personalized image was retrieved in existing systems by using tag-annotation-demand ranking for image retrieval (TAD) where image uploading, query searching, and page refreshing steps were taken place. In the proposed work, both the image and web page are retrieved by several techniques. Two major steps are followed in this work, where the primary step is server database upload. Herein, database for both image and content are stored using block acquiring page segmentation (BAPS). The subsequent step is to extract the image and content from the respective server database. The subsequent database is further applied into semantic annotation based clustering (SANB) (for image) and semantic based clustering (SEB) (for content). The experimental results show that the proposed approach accurately retrieves both the images and relevant pages.

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