Abstract
This chapter addresses a mass of data, possibly collected over years and on which structured query language queries have been repeatedly made to the point that one might not think there is any more information that can be gleaned from further mining; but it is the artificial neural network set of tools that come into play to discover the interactions and relationships existent among the data that are unknown. The rules that connect the various sets of data within the database may be fuzzy and dynamic. As the data submitted to the neural network are updated, it will adjust its “rules” in accordance, integrating the old data with the new, permitting us to correctly generalize new, dirty, incomplete, or future data.
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- 1.
Title: Glass identification database.
Sources:
(a) Creator: B. German, Central Research Establishment, Home Office Forensic Science Service, Aldermaston, Reading, Berkshire RG7 4PN.
(b) Donor: Vina Spiehler, Ph.D., DABFT, Diagnostic Products Corporation, (213) 776–0180 (ext 3014).
(c) Date: September, 1987. Past Usage: Rule Induction in Forensic Science, Ian W. Evett and Ernest J. Spiehler, Central Research Establishment, Home Office Forensic Science Service, Aldermaston, Reading, Berkshire RG7 4PN. Unknown technical note number. General results: nearest neighbor held its own with respect to the rule-based system. Relevant information: Vina conducted a comparison test of her rule-based system, Beagle, the nearest-neighbor algorithm, and discriminant analysis. Beagle is a product available through VRS Consulting, Inc.; 4676 Admiralty Way, Suite 206; Marina Del Ray, CA 90292 (213) 827–7890 and FAX: 3189.
- 2.
The results from LDA, NN, and Beagle are taken from literature (see note 1). The Supervised ANNs is an artificial organism created at Semeion Research Center for Sciences of Communication, via Sersale 117–119, 00128, Rome, Italy, (see www.semeion.it).
- 3.
Fuzziness is a new branch of mathematics by which degrees of involvement or belief can be determined. An example of fuzziness can perhaps best be described by the activities that occur during jury deliberation. A defendant must be judged guilty or not guilty. There is no ambiguity in this decision; everyone must come to the same conclusion. Even if someone is only 51 % sure of the degree of guilt or innocence, that juror must side entirely with either the guilty or not guilty position. It is the middle ground between guilty and not guilty that fuzzy theory plays its role. One can make a determination from 0 to 100 % as to their degree of belief.
- 4.
In Fig. 5.9, Lance and George are overlapped; the same happens for Ol and Phil.
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Software
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Buscema, M. (2013). Artificial Adaptive Systems in Data Visualization: Proactive Data. In: Buscema, M., Tastle, W. (eds) Intelligent Data Mining in Law Enforcement Analytics. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4914-6_5
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