… The CRISP-DM (CRoss Industry Standard Process for Data … CRISP-DM methodology in
a response modeling application project. The final goal of the project was to specify a process …

… algorithm for Data Mining has been analyzed and processed. The aim of this study is to …
CRISP-DM, and specially to process, understand and prepare information in the process of data …

… and more manageable, but small-scale data mining investigations also benefit from using
CRISP-DM. The aim of this study was to assess the applicability of knowledge discovery in …

… CRISP-DM is still fit for purpose for data science projects. We argue that if the project is
goal-directed and process-driven the process … CRoss-Industry Standard Process for Data Mining, …

… This method is one of the most famous data mining modeling. The use of this methodology
includes six phases that describe the data science application cycle. CRISP-DM is the …

… implementation of the Cross-Industry Standard Process for Data Mining (CRISP-DM) has …
plan through the data mining tracer study objective. Following the CRISP-DM steps, tracer …

… the data mining goals, and producing the project plan. In the KDDM process described in this
article, CRISP-DM … the data of network customers. Specific target can be refined as follows: …

… procedures to be followed. Among them, CRISP-DM (Cross Industry Standard Process for
Data Mining… subsequent categorization of each of the CRISP-DM steps, detailing the authors' …

… In conclusion, based on mitigation strategies and walk-around mechanisms used by
practitioners to address the standard process gaps, and referring to CRISP-DM process model, we …

… CRISP-DM included in this paper form an initial evaluation of the potential and effectiveness
of DM in these circumstances. Future research will consist of broadening the scope of …