## 3 Literature Review  
### State of the Art Review of Data Mining  
In 1996, the Cross-Industry Standard Process for Data Mining (CRISP-DM) was conceived, providing a \"blueprint for conducting a data mining project\" [29]. Data mining may be described as \"the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner\" [30]. CRISP-DM provides a structured six phase approach with highly flexible transitions between the phases, denoted by the multiple arrows and cyclical nature outlined in Figure 3a. CRISP-DM adequately satisfied the needs of data analysts and became established as the de facto standard for industry [29]. In 2000, [PERSON] envisaged that extensions and improvements were to be expected [29], and this evolution was analyzed in 2019 by [PERSON] et al. [31]. Figure 3: Data analysis models: (**a**) The CRISP-DM process model of data mining [32]; (**b**) DST Map [31]. Figure 2: Data Issue Map. The evolutions satisfy specific needs while retaining the principles and ideas of CRISPR-DM or Knowledge Discovery in Databases (KDD). In 1996, [PERSON] et al. [32] were of the opinion that \"KDD refers to the overall process of discovering useful knowledge from data, and data mining refers to a particular step in this process\". However, in more recent times, data mining is often used as a synonym for KDD [31], and we select data mining as the preferred term in this paper. Figure 4 outlines some of the most influential methodologies in the development of CRISPR-DM, along with some adaptations that it has inspired. Figure 4 is an adaptation of the evolution illustrated by [PERSON] et al. [33] which was released in 2010. [PERSON] et al. [33] name KDD and CRISP-DM as the two main approaches that are referred to as the \"canonical\" methodologies by [PERSON] et al. [31], depicted in grey in Figure 4. The most recent adaptations appear to be CRISP-DM related, addressing a variety of specific needs, such as the project management need in IBM (ASUM-DM [34]), the need for collaboration between geographically diverse groups (RAMSYS [35]) and the need for context change (CASP-DM [36]). More recently in 2020, [PERSON] et al. [37] performed a systematic literature review of data mining adaptations, categorizing the derivative work as modification, extension and integration-type adaptations. Modifications aim at solving a particular problem in a given case study, while extensions are aimed at altering data mining methodologies to account for specialized environments and incorporate context-awareness [37]. In contrast, integration-type adaptations are aimed at combining data mining methods with other domain frameworks, methodologies and concepts [37]. Prior to 2008, most data mining methods (CRISP-DM) were applied \"as is\", whereas the use of adapted data mining methodologies slowly began to take precedence in recent years [37]. Therefore, a key finding of the literature review is that data mining methodologies now need to be framed as part of \"a broader ecosystem of methodologies\", rather than the traditional, isolated implementation [37]. A change in the data mining landscape is also observed by [PERSON] et al. [31], noting that the term \"data science\" is preferred to the use of data mining. [PERSON] et al. [31] make the argument that \"the key difference (we perceive) between Data Mining twenty years ago and data science today is that the former is goal-oriented and concentrates on the process, while the latter is data-oriented and exploratory\". The term exploratory data analysis (EDA), a closely related term to data mining, was first introduced in 1977 by [PERSON] [38] to describe the detective-like analysis of data--that is to say, to not take the data at face value or as absolute truth. Therefore, it is important to understand the data  
Figure 4: Evolution of Data Mining and Data Science models and methodologies [31].