TITLE: Adapting CRISP-DM for Idea Mining: A Data Mining Process for Generating Ideas Using a Textual Dataset 

ABSTRACT: Data mining project managers can benefit from using standard data mining
process models. The benefits of using standard process models for data mining,
such as the de facto and the most popular, Cross-Industry-Standard-Process
model for Data Mining (CRISP-DM) are reduced cost and time. Also, standard
models facilitate knowledge transfer, reuse of best practices, and minimize
knowledge requirements. On the other hand, to unlock the potential of
ever-growing textual data such as publications, patents, social media data, and
documents of various forms, digital innovation is increasingly needed.
Furthermore, the introduction of cutting-edge machine learning tools and
techniques enable the elicitation of ideas. The processing of unstructured
textual data to generate new and useful ideas is referred to as idea mining.
Existing literature about idea mining merely overlooks the utilization of
standard data mining process models. Therefore, the purpose of this paper is to
propose a reusable model to generate ideas, CRISP-DM, for Idea Mining
(CRISP-IM). The design and development of the CRISP-IM are done following the
design science approach. The CRISP-IM facilitates idea generation, through the
use of Dynamic Topic Modeling (DTM), unsupervised machine learning, and
subsequent statistical analysis on a dataset of scholarly articles. The adapted
CRISP-IM can be used to guide the process of identifying trends using scholarly
literature datasets or temporally organized patent or any other textual dataset
of any domain to elicit ideas. The ex-post evaluation of the CRISP-IM is left
for future study.
Data mining project managers can benefit from using standard data mining
process models. The benefits of using standard process models for data mining,
such as the de facto and the most popular, Cross-Industry-Standard-Process
model for Data Mining (CRISP-DM) are reduced cost and time. Also, standard
models facilitate knowledge transfer, reuse of best practices, and minimize
knowledge requirements. On the other hand, to unlock the potential of
ever-growing textual data such as publications, patents, social media data, and
documents of various forms, digital innovation is increasingly needed.
Furthermore, the introduction of cutting-edge machine learning tools and
techniques enable the elicitation of ideas. The processing of unstructured
textual data to generate new and useful ideas is referred to as idea mining.
Existing literature about idea mining merely overlooks the utilization of
standard data mining process models. Therefore, the purpose of this paper is to
propose a reusable model to generate ideas, CRISP-DM, for Idea Mining
(CRISP-IM). The design and development of the CRISP-IM are done following the
design science approach. The CRISP-IM facilitates idea generation, through the
use of Dynamic Topic Modeling (DTM), unsupervised machine learning, and
subsequent statistical analysis on a dataset of scholarly articles. The adapted
CRISP-IM can be used to guide the process of identifying trends using scholarly
literature datasets or temporally organized patent or any other textual dataset
of any domain to elicit ideas. The ex-post evaluation of the CRISP-IM is left
for future study.
Adapting CRISP-DM for Idea Mining: A Data Mining Process for Generating Ideas Using a Textual Dataset
http://arxiv.org/abs/2105.00574v1