A Simple Dynamic Mind-map Framework To Discover Associative Relationships in Transactional Data Streams
| dc.creator | Schommer, Christoph | |
| dc.date | 2008-05-09 | |
| dc.date.accessioned | 2026-07-07T12:18:49Z | |
| dc.date.available | 2026-07-07T12:18:49Z | |
| dc.description | In this paper, we informally introduce dynamic mind-maps that represent a new approach on the basis of a dynamic construction of connectionist structures during the processing of a data stream. This allows the representation and processing of recursively defined structures and avoids the problem of a more traditional, fixed-size architecture with the processing of input structures of unknown size. For a data stream analysis with association discovery, the incremental analysis of data leads to results on demand. Here, we describe a framework that uses symbolic cells to calculate associations based on transactional data streams as it exists in e.g. bibliographic databases. We follow a natural paradigm of applying simple operations on cells yielding on a mind-map structure that adapts over time. | |
| dc.description | 12 pages, 8 Figures. Updated version of a paper presented at the Workshop on Symbolic Networks, ECAI 2004, Valencia, Spain | |
| dc.identifier | https://arxiv.org/abs/0805.1296 | |
| dc.identifier | http://arxiv.org/abs/0805.1296 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/212540 | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.subject | Symbolic Computation | |
| dc.subject | I.2.6; H.2.8 | |
| dc.title | A Simple Dynamic Mind-map Framework To Discover Associative Relationships in Transactional Data Streams | |
| dc.type | text |