Download Knowledge Discovery Process and Methods to Enhance by Kweku-Muata Osei-Bryson, Corlane Barclay PDF

By Kweku-Muata Osei-Bryson, Corlane Barclay

Supplying insights into the scope of knowledge mining projects, this article examines their socio-economic and felony implications to stakeholders, firms and society.

summary: supplying insights into the scope of information mining projects, this article examines their socio-economic and felony implications to stakeholders, firms and society

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Choi et al. 5 Data Mining Success Criteria for Directed DM Problem Type a DMSC Classification Accuracy, precision, recall, profit and loss, lift, simplicity,a stability, speed, training time, and memory usage Estimation Mean square error, variance (standard deviation), simplicity,a stability, speed, training time, and memory usage Simplicity is not relevant in case of nonexplanatory, black box models. 6 DMSC for Undirected DM Problem Type DMSC Clustering Normalized cluster mean, variable importance vectors, and outliers and usefulness Association rules Lift, simplicity (rule length), support, confidence, recall, precision, interest factor, expected monetary factor, and incremental monetary factor Description or visualization Number of instances in dataset, number of dimensions, overlapping data instances, ability to reveal patterns in dataset, ability to reveal clusters of two or three dimensions, number of clusters present, amount of background noise, variance of clusters, ability to manipulate display automatically, and ease of use presented approaches for prioritizing association rules.

A list of errors made in the process is prepared. Using the Discovered Knowledge This step consists of planning where and how the discovered knowledge will be used. The application area in the current domain should be extended to other domains. A plan to monitor the implementation of the discovered knowledge should be created, and the entire project documented. Cios and Kurgan (2005) also specify four additional feedback loops as compared to the CRISP-DM model and the situations under which the loops would get triggered.

The specific activities in this step depend very much on the kind of application that is being developed. However, the main objective remains the same, that is, to interpret and to evaluate the output from the DM step. Step 5—Assimilation of knowledge: This step closes the loop, which was opened when the business objectives were set at the beginning of the process. The objective now is to put into action the commitments made in the opening step, according to the new, valid, and actionable information from the previous process steps.

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