Bournaud, I. & Ganascia, J.G. (1997). Accounting for Domain Knowledge in the Construction of a Generalization Space. ICCS, Lectures Notes in AI n1257, Springer-Verlag, pp. 446-459.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....measures were developed and utilized for ranking the interestingness of generalized relations using DGGs, they are more generally applicable to other problem domains. For example, alternative methods could be used to guide the generation of summaries, such as Galois lattices [6] conceptual graphs [3], or formal concept analysis [19] Also, summaries could more generally include views generated from databases or summary tables generated from data cubes. However, we do not dwell here on the methods or technical aspects of deriving summaries, views, or summary tables. Instead, we simply refer ....

I. Bournaud and J.-G. Ganascia. Accounting for domain knowledge in the construction of a generalization space. In Proceedings of the Third International Conference on Conceptual Structures, pages 446--459. Springer-Verlag, August 1997.

....of a learning method based on a GL. Godin, Missaoui, and Alaoui s work on incremental concept formation is based on Galois lattices, a type of GL used in discrete mathematics [5] Bournaud and Ganascia investigated the automatic creation of a GL from a set of objects described by conceptual graphs[1]. In data mining, GLs are used to conceptualize the process of generalizing data as a transformation of values from one domain to values of another, smaller domain. The original data, as retrieved from a database or other source, is considered the most specific representation of the data. ....

I. Bournaud and J.-G. Ganascia. Accounting for domain knowledge in the construction of a generalization space. In Proceedings of the Third International Conference on Conceptual Structures, pages 446--459. Springer-Verlag, August 1997.

....Item 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 1 2 1 1 1 1 1 1 1 2 1 Figure 1. A data cube Of course, numerous methods could be used to guide the generation of summaries, such as concept hierarchies [5] domain generalization graphs [15] Ga lois lattices [9] conceptual graphs [4], and formal concept analysis [22] Also, summaries could more generally include many other forms of knowledge representation, such as database views, association rules, itemsets, and web search results. However, when given hundreds, or even thousands of summaries (possibly multi dimensional) it ....

I. Bournaud and J.-G. Ganascia. Accounting for domain knowledge in the construction of a generalization space. In Proceedings of the Third International Conference on Conceptual Structures, pages 446--459. Springer-Verlag, August 1997.

....and DGGs for mining large industrial databases is described in [15, 16] Although this example is based upon summaries generated from databases using AOG and DGGs, alternative methods could be used to guide the generation of summaries. These include Galois lattices [9] conceptual graphs [4], or formal concept analysis [24] Similarly, summaries could more generally include views generated from databases, characterized generalized association rules generated from itemsets, or summary tables (i.e. data cubes) generated from data warehouses. Regardless of the representation language ....

I. Bournaud and J.-G. Ganascia. Accounting for domain knowledge in the construction of a generalization space. In Proceedings of the Third International Conference on Conceptual Structures, pages 446--459. Springer-Verlag, August 1997.

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Bournaud, I. & Ganascia, J.G. (1997). Accounting for Domain Knowledge in the Construction of a Generalization Space. ICCS, Lectures Notes in AI n1257, Springer-Verlag, pp. 446-459.

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Bournaud I. & Ganascia J.-G. (1997). Accounting for Domain Knowledge in the Construction of a Generalization Space. ICCS'97, Lectures Notes in AI n1257, Springer-Verlag, 446-459.

.... for a partial matching of an arc from G1 with an arc from G2 [1] This restriction has been previously used in [23] It has the advantage of limiting the complexity of the algorithm (in the worst case quadratic with the number of objects [1] because, as the arcs are oriented they fully match [2]. However, this restricts the generalization language since the relations among arcs are not considered. The COING principle for building the GS is as follows: 1. Reformulate each graph describing the objects to be organized as a set of arcs. 2. Generalize each arc describing the objects. COING ....

....The COING principle for building the GS is as follows: 1. Reformulate each graph describing the objects to be organized as a set of arcs. 2. Generalize each arc describing the objects. COING integrates an efficient method for taking into account domain knowledge in the GS construction [2]. This knowledge, represented in a generalization hierarchy (called the type lattice in the conceptual graphs formalism [25] expresses, for example in the domain of colours, that the type Black and White (noted B W) is a generalization of the three types White, Black and Gray. Figure 2 below ....

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Bournaud I., Ganascia J.-G.: Accounting for Domain Knowledge in the Construction of a Generalization Space. ICCS'97, Lectures Notes in AI n1257, Springer-Verlag, pp. 446459. (1997).

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