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Khan, Asif Ullah
- Clustering of Web Page for Different Domains Using Data Extraction and Self Organizing Map
Abstract Views :193 |
PDF Views:3
Authors
Affiliations
1 C.S.E., Technocrats Institute of Technology, Bhopal, M.P., IN
2 Dept. of C.S.E., Technocrats Institute of Technology, Bhopal, M.P., IN
3 Dept of C.S.E., Technocrats Institute of Technology, Bhopal, M.P., IN
1 C.S.E., Technocrats Institute of Technology, Bhopal, M.P., IN
2 Dept. of C.S.E., Technocrats Institute of Technology, Bhopal, M.P., IN
3 Dept of C.S.E., Technocrats Institute of Technology, Bhopal, M.P., IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 12 (2013), Pagination: 432-437Abstract
Given the rapid growth and success of public information sources on the World Wide Web, it is increasingly attractive to extract data from these sources and make it available for further processing by end users and application programs. Data extracted from Web sites can serve as the springboard for a variety of tasks, including information retrieval (e.g. business intelligence), event monitoring (news and stock market), and electronic commerce (shopping comparison). Extracting structured data from Web sites is not a trivial task. Most of the information on the Web today is in the form of Hypertext Markup Language (HTML) documents which are viewed by humans with a browser. A sophisticated method to organize the layout of the information and assist user navigation is therefore particularly important. Data Extraction is the process of retrieving data out of data sources further data processing. Online data exists in the form of a web record. Depending on the end user query, the query results are generated by web databases and from this query results pages. The main objective of this paper is to extract and align important data from different domains with the help of HTML tags and its value. After extracting data, Self Organizing Map (SOM) will classify the extracted data from different domains in the form of clusters. Clustering is the process of grouping physical or abstract objects into classes of similar objects.Keywords
Data Extraction, Data Record Alignment, Clustering, QRR, SOM.
- A Weighted Markov Model for Web Pre-Fetching to Improve User Interface over Internet
Abstract Views :112 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Science and Engineering, TIT, Bhopal, IN
1 Department of Computer Science and Engineering, TIT, Bhopal, IN
Source
International Journal of Advanced Networking and Applications, Vol 5, No 3 (2013), Pagination: 1962-1967Abstract
Rapid growth of web application has increased the researcher's interests in this era. All over the world has surrounded by the computer network. There is a very useful application call web application used for the communication and data transfer. An application that is accessed via a web browser over a network is called the web application. Web caching is a well-known strategy for improving the performance of Web based system by keeping Web objects that are likely to be used in the near future in location closer to user. The Web caching mechanisms are implemented at three levels: client level, proxy level and original server level. Significantly, proxy servers play the key roles between users and web sites in lessening of the response time of user requests and saving of network bandwidth. Therefore, for achieving better response time, an efficient caching approach should be built in a proxy server. This paper use FP growth, weighted rule mining concept and Markov model for fast and frequent web pre fetching in order to has improved the hit ratio of the web page and expedites users visiting speed.Keywords
Web Services, Pre-Fetching, Log File.Full Text