- 期刊
- OpenAccess
Equipped Search Results Using Machine Learning from Web Databases
並列摘要
Aim of this study is to form a cluster of search results based on similarity and to assign meaningful label to it Database driven web pages play a vital role in multiple domains like online shopping, e-education systems, cloud computing and other. Such databases are accessible through HTML forms and user interfaces. They return the result pages come from the underlying databases as per the nature of the user query. Such types of databases are termed as Web Databases (WDB). Web databases have been frequently employed to search the products online for retail industry. They can be private to a retailer/concern or publicly used by a number of retailers. Whenever the user queries these databases using keywords, most of the times the user will be deviated by the search results returned. The reason is no relevance exists between the keyword and SRs (Search Results). A typical web page returned from a WDB has multiple Search Result Records (SRRs). An easier way is to group the similar SRRs into one cluster in such a way the user can be more focused on his demand. The key concept of this paper is XML technologies. In this study, we propose a novel system called CSR (Clustering Search Results) which extracts the data from the XML database and clusters them based on the similarity and finally assigns meaningful label for it. So, the output of the keyword entered will be the clusters containing related data items.