@inproceedings{f5d432109574403a826a8f7e9ada54cc,
title = "Discretization of Multidimensional Web Data for Informative Dense Regions Discovery",
abstract = "Dense regions discovery is an important knowledge discovery process for finding distinct and meaningful patterns from given data. The challenge in dense regions discovery is how to find informative patterns from various types of data stored in structured or unstructured databases, such as mining user patterns from Web data. Therefore, novel approaches are needed to integrate and manage these multi-type data repositories to support new generation information management systems. In this paper, we focus on discussing and purposing several discretization methods for large matrices. The experiments suggest that the discretization methods can be employed in practical Web applications, such as user patterns discovery.",
keywords = "Dense regions discovery, Discretization, Web information system, Web mining",
author = "Wu, {Edmond H.} and Ng, {Michael K.} and Yip, {Andy M.} and Chan, {Tony F.}",
note = "Publisher copyright: {\textcopyright} 2004 Springer-Verlag Berlin Heidelberg; First International Symposium on Computational and Information Science, CIS 2004 ; Conference date: 16-12-2004 Through 18-12-2004",
year = "2004",
month = dec,
day = "3",
doi = "10.1007/978-3-540-30497-5_112",
language = "English",
isbn = "9783540241270",
series = "Lecture Notes in Computer Science",
publisher = "Springer Berlin Heidelberg",
pages = "718--724",
booktitle = "Computational and Information Science",
url = "https://link.springer.com/book/10.1007/b104566",
}