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

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.

Author dc:creator

• Zhu, Xiao

• CRISP-DM
• Agile software development
• Data mining

Statement dc:rights

• The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
• info:eu-repo/semantics/openAccess
• au.edu.uts.lib/ppc

Language dc:language.iso

en_AU

Handle dc:identifier.uri

http://hdl.handle.net/10453/123178

OAI identifier oai:identifier

oai:opus.lib.uts.edu.au:10453/123178