Conference proceeding
Making better sense of the demographic data value in the data mining procedure
Foundations and Novel Approaches in Data Mining, v 9, pp 331-362
01 Jan 2006
Abstract
Data mining of personal demographic data is being used as a weapon in the War on Terrorism, but we are forced to acknowledge that it is a weapon loaded with interpretations derived from the use of dirty data in inherently biased systems that mechanize and de-humanize individuals. While the unit of measure is the individual in a local context, the global decision context requires that we understand geolocal reflexive communal selves who have psychological and social/societal relationship patterns that can differ markedly and change over time and in response to pivotal events. Local demographic data collection processes fail to take these realities into account at the data collection design stage. As a result, existing data values rarely represent all individual's multi-dimensional existence in a Form that can be mined. An abductive approach to data mining can be used to improve the data inputs. Working from the "decision-in," we can identify and address challenges associated with dernographic data collection and suggest ways to improve the quality of the data available for the data mining procedure. It is important to note that exchanging old values for new values is rarely a 1:1 substitution where qualitative data is involved. Different constituent user populations may require different levels of data complexity and they will need to improve their understanding of the data values reported at the local level if they are to effectively relate various local dernographic databases in new and different global contexts.
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Details
- Title
- Making better sense of the demographic data value in the data mining procedure
- Creators
- K M ShelferX H Hu - Drexel University, Information Science
- Publication Details
- Foundations and Novel Approaches in Data Mining, v 9, pp 331-362
- Series
- Studies in Computational Intelligence
- Publisher
- Springer Nature
- Number of pages
- 2
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000235303500019
- Other Identifier
- 991019170576404721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence