Table 3.

First design steps for the framework for the development of data-driven Mamdani-type CDSS.

DDMTFCDSS Activity Steps	Activity Description
Identifying the problems and evaluating the complexity of the specific domain
1. To identify the dataset.	This stage is related to identifying the source of the collected data to make the fuzzy inference system. These data usually belong to experiments that seek to observe the behavior of some dependent variables through the interaction of independent variables. Generally, the first variables are known as output variables and the second are known as input variables. This section describes the context and the adopted methodology to obtain the database that will serve as an input to work with the other framework components.
2. Data Preparation (Crisp inputs).	This step, according to the methodology proposed by Palit and Popovic [75] and modified by Cavalcante et al. [76], means that the data stored in multiple data sources (Spreadsheets, Data Bases—DBs, Comma-Separated Values—CSV, Enterprise Resource Planning—ERP, Customer Relationship Managers—CRM, Material Requirements Planning—MRP, among others) must be pre-processed [76]. The first part of this phase is to define the input and output variables that will be used for modeling. The pre-processing is a procedure where datasets (input(s) and output(s)) are prepared to be processed by the data mining technique (clusters) and the computational intelligence (fuzzy system). For doing that, in the literature, some pre-processing mechanisms used to improve the prediction or classification performance, among these, we can found Feature selection [77], Feature Extraction [78], de-noising, outlier detection [79], Time series segmentation [80], and Clustering [81]. Datasets must be normalized and structured. This can be done with the help of spreadsheet software like Microsoft Excel® among others.
3. Reviewing existing models.	In this stage, an academic and scientific search of the different works related to the problem is carried out. For this, different indexed databases such as Scopus, Science Direct, Web of Science, Scielo, Google Scholar, ACM, etc. are used.