Analysis of Valuable Clustering Techniques for Deep Web Access and Navigation

A massive amount of content is available on web but huge portion of it is still invisible. User can only access this hidden web, also called Deep web, by entering a directed query in a web search form and thus accessing the data from database which is not indexed with hyperlinks. Inability to index particular type of content and restricted storage capacity is significant factor behind the invisibleness of web content. Different clustering techniques offer a simple way to analyze large volume of non-indexed content. The major focus of research is to analyze the different clustering techniques to find more accurate and efficient method for accessing and navigating the deep web content. Analysis and comparison of Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and Hierarchical and K-means method have been carried out and valuable factors for clustering in deep web have been identified.

@article{ain2018,
  title     = {Analysis of Valuable Clustering Techniques for Deep Web Access and Navigation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {1},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Qurat ul ain and Asma Sajid and Uzma Jamil},
  doi       = {10.14569/IJACSA.2018.090128},
  url       = {https://doi.org/10.14569/IJACSA.2018.090128}
}