Applying visual analytics on traditional data mining process: Quick prototype, simple expertise transformation, and better interpretation

Xiao ZHU, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

1 Citation (Scopus)

Abstract

Due to a lack of experience, business might not be confident about the completeness of their proposed data mining (DM) project objectives at early stage. Besides, business domain expertise usually shrinks when delivered to data analysts. This expertise ought to contribute more throughout whole project. In addition, the outcome from DM project might fail to transform into actionable advice as the interpretation for the outcome is hard to understand and, as a result, unconvincing to apply in real. To fill the above three gaps, Visual Analytics (VA) tools are applied in different stages to optimize traditional data analytics process. In my practice, VA tools have offered both an easy access to generate quick insights for evaluating project objective's viability, and a bidirectional channel between data analysts and stakeholders to break the background barrier. Consequently, more applicable outcomes and better client satisfaction are gained. Copyright © 2016 IEEE.

Original languageEnglish
Title of host publicationProceedings of 4th International Conference on Enterprise Systems, ES
EditorsGang LI, Yale YU
PublisherIEEE
Pages208-213
ISBN (Electronic)9780769559841
DOIs
Publication statusPublished - Mar 2017

Citation

Zhu, X. & Xu, G. (2016). Applying visual analytics on traditional data mining process: Quick prototype, simple expertise transformation, and better interpretation. In G. Li & Y. Yu (Eds.), Proceedings of 4th International Conference on Enterprise Systems, ES (pp. 208-213). https://doi.org/10.1109/ES.2016.34

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