Catálogo de publicaciones - libros

Spectral methods, as an unsupervised technique, have been used with success in data mining such as LSI in information retrieval, HITS and PageRank in Web search engines, and spectral clustering in machine learning. The essence of success in these applications is the spectral information that captures the semantics inherent in the large amount of data required during unsupervised learning. In this paper, we ask if spectral methods can also be used in supervised learning, e.g., classification. In an attempt to answer this question, our research reveals a novel kernel in which spectral clustering information can be easily exploited and extended to new incoming data during classification tasks. From our experimental results, the proposed Spectral Kernel has proved to speedup classification tasks without compromising accuracy.

Data Warehousing and Knowledge Discovery has been widely accepted as a key technology for enterprises to improve their abilities in data analysis, decision support, and the automatic extraction of knowledge from data. Historically, the phrase knowledge discovery in databases was coined at the first KDD (Knowledge Discovery and Data Mining) workshop in 1989 to emphasize that knowledge is the end-product of a data-driven discovery process. Since then, much research has been accomplished in this field. This paper which is written as an epilogue of the DaWaK 2005 proceedings by the programme committee chairpersons together with Nguyen Manh Tho, should reflect the past development of DaWaK-results and other significant research outcomes in the area and above all should deliver a rough sketch of the current development and possible future work.