Title: | Multidimensional on-line mining |
Authors: | Wang, CY Hong, TP Tseng, SS 資訊工程學系 Department of Computer Science |
Keywords: | data mining;association rule;incremental mining;multi-dimensional mining;constraint-based mining;data warehouse |
Issue Date: | 2006 |
Abstract: | In the past;, incremental mining approaches usually considered getting the newest; set; of knowledge consistent; with the entire set of data inserted so far. Users can not, however, use them to obtain rules or patterns only from their interesting portion of the data. In addition, these approaches only focused on finding frequent patterns in a specified part of a database. That is, although the data records are collected in under certain time, place and category, such contexts (circumstances) have been ignored in conventional mining algorithms. It will cause the lack of patterns or rules to help users solve problems at, different, aspects and with diverse considerations. In this paper, we thus attempt; to extend incremental mining to online decision support under multidimensional context considerations. We first propose the multidimensional pattern relation to structurally and systematically retain the additional context information and mining information for each inserted dataset into a database. We then develop an algorithm based on the proposed multidimensional pattern relation to correctly and efficiently fulfill diverse on-fine mining requests. |
URI: | http://hdl.handle.net/11536/17340 |
ISBN: | 3-540-28315-3 |
ISSN: | 1860-949X |
Journal: | Foundations and Novel Approaches in Data Mining |
Volume: | 9 |
Begin Page: | 243 |
End Page: | 257 |
Appears in Collections: | Conferences Paper |