Learning task models in ill-defined domain using an hybrid knowledge discovery framework

Nkambou, Roger; Fournier-Viger, Philippe; Nguifo, Engelbert Mephu

doi:10.1016/j.knosys.2010.08.002

Cited by 19 publications

(11 citation statements)

References 34 publications

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“…In real world development, there exist cases in non-business domains (Mansmann, 2009) where the data warehouse developer needs to design schema where an operational system does not exist. Furthermore, such domains may be ill-defined domains (Nkambou et al, 2011), thus compounding the difficulty of the design problem. Therefore, their research is similar to the work undertaken in this paper as we also target those cases where limited domain knowledge exists and no operational system is in place.…”

Section: Automating the Design And Construction Of Multidimensional Smentioning

confidence: 99%

Discovering diverse association rules from multidimensional schema

Usman

Pears

Fong

2013

Expert Systems with Applications

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The integration of data mining techniques with data warehousing is gaining popularity due to the fact that both disciplines complement each other in extracting knowledge from large datasets. However, the majority of approaches focus on applying data mining as a front end technology to mine data warehouses. Surprisingly, little progress has been made in incorporating mining techniques in the design of data warehouses. While methods such as data clustering applied on multidimensional data have been shown to enhance the knowledge discovery process, a number of fundamental issues remain unresolved with respect to the design of multidimensional schema. These relate to automated support for the selection of informative dimension and fact variables in high dimensional and data intensive environments, an activity which may challenge the capabilities of human designers on account of the sheer scale of data volume and variables involved. In this research, we propose a methodology that selects a subset of informative dimension and fact variables from an initial set of candidates. Our experimental results conducted on three real world datasets taken from the UCI machine learning repository show that the knowledge discovered from the schema that we generated was more diverse and informative than the standard approach of mining the original data without the use of our multidimensional structure imposed on it.

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Section: Automating the Design And Construction Of Multidimensional Smentioning

confidence: 99%

Discovering diverse association rules from multidimensional schema

Usman

Pears

Fong

2013

Expert Systems with Applications

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“…In most computer games such as real-time strategy games [19,20], the problem of determining a path that is the shortest and the safest one between two points in a map, where there exist threat sources that can harm the traveling agent, is a complex one. It cannot be solved within the allowed path search algorithm execution time limit of such a computer game, where the execution time is usually in the order of a few ten milliseconds.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Using the value of DiCF as 1.0 essentially indicates the usage of optimal distance heuristic of A ⁄ for the distance portion of the whole heuristic. On the other hand, Damage Contribution Factor (DCF) was chosen to vary as 0.0, 0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 2, 3, 4, 5, 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,30,40, 50, 60, 70, 80, 90, 100, 1000, 10000 and 100000. Very large and very small numbers were used for this factor, in order to observe the effect of dominance of one part of the total heuristic (distance or damage) on the whole.…”

Section: Preliminary Testsmentioning

confidence: 99%

Limited-Damage A*: A path search algorithm that considers damage as a feasibility criterion

Bayili

Polat

2011

Knowledge-Based Systems

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“…Nkambou et al [20] proposed a procedural-knowledge acquisition framework based on a combination of sequential pattern mining and association rules discovery techniques. This framework aims at providing Intelligent Tutoring Systems with relevant domain knowledge that enable them to guide the learner during problem-solving learning activities for ill-defined domains where this knowledge is hard to be defined explicitly by domain experts.…”

Section: Related Workmentioning

confidence: 99%