An attribute–value system is a basic knowledge representation framework comprising a table with columns designating "attributes" (also known as "properties", "predicates", "features", "dimensions", "characteristics", "fields", "headers" or "independent variables" depending on the context) and "rows" designating "objects" (also known as "entities", "instances", "exemplars", "elements", "records" or "dependent variables"). Each table cell therefore designates the value (also known as "state") of a particular attribute of a particular object.

Example of attribute–value system

Below is a sample attribute–value system. It represents 10 objects (rows) and five features (columns). In this example, the table contains only integer values. In general, an attribute–value system may contain any kind of data, numeric or otherwise. An attribute–value system is distinguished from a simple "feature list" representation in that each feature in an attribute–value system may possess a range of values (e.g., feature P₁ below, which has domain of {0,1,2}), rather than simply being present or absent (Barsalou & Hale 1993).

Sample attribute–value system

Object	P1	P2	P3	P4	P5
O1	1	2	0	1	1
O2	1	2	0	1	1
O3	2	0	0	1	0
O4	0	0	1	2	1
O5	2	1	0	2	1
O6	0	0	1	2	2
O7	2	0	0	1	0
O8	0	1	2	2	1
O9	2	1	0	2	2
O10	2	0	0	1	0

Other terms used for "attribute–value system"

Attribute–value systems are pervasive throughout many different literatures, and have been discussed under many different names:

• Flat data
• Spreadsheet
• Attribute–value system (Ziarko & Shan 1996)
• Information system (Pawlak 1981)
• Classification system (Ziarko 1998)
• Knowledge representation system (Wong & Ziarko 1986)
• Information table (Yao & Yao 2002)

See also

• Bayes networks
• Entity–attribute–value model
• Formal concept analysis
• Joint distribution
• Knowledge representation
• Optimal classification (in Wikibooks)
• Rough set
• Triplestore

References

• Barsalou, Lawrence W.; Hale, Christopher R. (1993). "Components of conceptual representation: From feature lists to recursive frames". In Iven Van Mechelen; James Hampton; Ryszard S. Michalski; Peter Theuns (eds.). Categories and Concepts: Theoretical Views and Inductive Data Analysis. London: Academic Press. pp. 97–144. ISBN 9780127141756.
• Pawlak, Zdzisław (1991). Rough sets: Theoretical Aspects of Reasoning about Data. Dordrecht: Kluwer.
• Ziarko, Wojciech; Shan, Ning (1996). "A method for computing all maximally general rules in attribute–value systems". Computational Intelligence. 12 (2): 223–234. doi:10.1111/j.1467-8640.1996.tb00260.x. S2CID 7200948.
• Pawlak, Zdzisław; Shan, Ning (1981). "Information systems: Theoretical foundations". Information Systems. 6 (3): 205–218. doi:10.1016/0306-4379(81)90023-5.
• Wong, S. K. M.; Ziarko, Wojciech; Ye, R. Li (1986). "Comparison of rough-set and statistical methods in inductive learning". International Journal of Man-Machine Studies. 24: 53–72. doi:10.1016/S0020-7373(86)80033-5.
• J. T., Yao; Yao, Y. Y. (2002). "Induction of classification rules by granular computing". Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing (TSCTC'02). London, UK: Springer-Verlag. pp. 331–338.
• Watanabe, Satosi (1985). Pattern Recognition: Human and Mechanical. New York: John Wiley & Sons.
• Ziarko, Wojciech (1998). "Rough sets as a methodology for data mining". In Polkowski, Lech; Skowron, Andrzej (eds.). Rough Sets in Knowledge Discovery 1: Methodology and Applications. Heidelberg: Physica-Verlag. pp. 554–576.