Agile mining : a novel data mining process for industry practice based on Agile Methods and visualization

Publication Type:
Thesis
Issue Date:
2017
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Current standard data mining processes like CRoss-Industry Standard Process for Data mining (CRISP-DM) are vulnerable to frequent change of customer requirement. Meanwhile, Stakeholders might not acquire sufficient understanding to generate business value from analytic results due to a lack of intelligible explanatory stage. These two cases repeatedly happen on those companies which are inexperienced in data mining practice. Towards this issue, Agile Mining, a refined CRISP-DM based data mining (DM) process, is proposed to address these two friction points between current data mining processes and inexperienced industry practitioners. By merging agile methods into CRISP-DM, Agile Mining process achieves a requirement changing friendly data mining environment for inexperienced companies. Moreover, this Agile Mining transforms traditional analytic-oriented evaluation to business-oriented visualization-based evaluation. In the case study, two industrial data mining projects are used to illustrate the application of this new data mining process and its advantages.
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