Methods and Applications of Data Mining in Business Domains

This Special Issue invited researchers to contribute original research in the field of data mining, particularly in its application to diverse domains, like healthcare, software development, logistics, and human resources. We were especially interested in how the data mining method was modified to cater to the specific domain in question. The challenge is that the more complex a domain is the harder it is to make good predictions, as more implicit domain knowledge is required that is not always available [1]. This is especially true in the case of complex domains where there are soft factors, like the interaction of the conflicting and cooperating objectives of the stakeholders [2,3], and system dynamics play a significant role [4]. In a business context, the challenge is that one would like to see (i) how the algorithms can be repeatable in the real world, (ii) how the patterns mined can be utilized by the business, and (iii) how the resulting model can be understood and utilized in the business environment [1]. Furthermore, the idea is to identify the variables that impact the goal variable but to do so with the data, interestingness, deployment, and general domain (business) constraints of the domain [1,5].
One of the methods to analyze a complex domain is using a method called intelligence meta-synthesis [6,7]. Intelligence synthesis is the collection and creation of perceived or understood (i.e., not necessarily objective) information. Meta-synthesis is the collection and creation of knowledge and information from collected intelligences [1]. The goal of this approach is to design and develop predictive models that could eventually be incorporated into a business intelligence dashboard. As a result, one would (i) understand the nature and origin of data that allows the system user to determine the quality of the data to perform the data cleaning; (ii) understand the factors in the domain that influence the predicted variable, leading the developer to determine which variables need to be included in the predictive model; (iii) develop predictive models that are usable and interesting within the domain in terms of predictive power, integrating with existing infrastructure, and integrating with business rules and processes; and finally (iv) use the predicted data to find the optimal business processes in the particular domain. There are also research works that have built on top of intelligence meta-synthesis, such as the study published by the authors of [1].