@inproceedings{ed170df45e7445a98ce79369c39fa1a4,
title = "Identifying relevant databases for multidatabase mining",
abstract = "Various tools and systems for knowledge discovery and data mining are developed and available for applications. However, when we are immersed in heaps of databases, an immediate question facing practitioners is where we should start mining. In this paper, breaking away from the conventional data mining assumption that many databases be joined into one, we argue that the first step for multidatabase mining is to identify databases that are most likely relevant to an application; without doing so, the mining process can be lengthy, aimless and ineffective. A relevance measure is thus proposed to identify relevant databases for mining tasks with an objective to find patterns or regularities about certain attributes. An efficient implementation for identifying relevant databases is described. Experiments are conducted to validate the measure{\textquoteright}s performance and to show its promising applications.",
keywords = "Data mining, Multiple databases, Query, Relevance measure",
author = "Huan Liu and Hongjun Lu and Jun Yao",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1998.; 2nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 1998 ; Conference date: 15-04-1998 Through 17-04-1998",
year = "1998",
doi = "10.1007/3-540-64383-4_18",
language = "English (US)",
isbn = "3540643834",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "210--221",
editor = "Xindong Wu and Ramamohanarao Kotagiri and Korb, {Kevin B.}",
booktitle = "Research and Development in Knowledge Discovery and Data Mining - 2nd Pacific-Asia Conference, PAKDD 1998, Proceedings",
}