Intelligent User Assistance for Automated Data Mining Method Selection

Abstract

In any data science and analytics project, the task of mapping a domain-specific problem to an adequate set of data mining methods by experts of the field is a crucial step. However, these experts are not always available and data mining novices may be required to perform the task. While there are several research efforts for automated method selection as a means of support, only a few approaches consider the particularities of problems expressed in the natural and domain-specific language of the novice. The study proposes the design of an intelligent assistance system that takes problem descriptions articulated in natural language as an input and offers advice regarding the most suitable class of data mining methods. Following a design science research approach, the paper (i) outlines the problem setting with an exemplary scenario from industrial practice, (ii) derives design requirements, (iii) develops design principles and proposes design features, (iv) develops and implements the IT artifact using several methods such as embeddings, keyword extractions, topic models, and text classifiers, (v) demonstrates and evaluates the implemented prototype based on different classification pipelines, and (vi) discusses the results' practical and theoretical contributions. The best performing classification pipelines show high accuracies when applied to validation data and are capable of creating a suitable mapping that exceeds the performance of joint novice assessments and simpler means of text mining. The research provides a promising foundation for further enhancements, either as a stand-alone intelligent assistance system or as an add-on to already existing data science and analytics platforms.

A nascent design theory for explainable intelligent systems

“…Looking at previous design science research reveal that the integration of AI in DSS leads to intelligent systems that are capable of supporting users in their decision-making process (Janiesch et al 2021). However, due to their focus on user performance, these systems are primarily developed for lowstake use cases wherein users do not rely on comprehending the reasoning of a ML model (e.g., Zschech et al 2020) as an incorrect recommendation has no significant impact on humans or the environment (Rudin 2019). In contrast, utilizing these systems in high-stake use cases, wherein incorrect decisions may endanger human lives or may have vast consequences, designing intelligent systems require the explicit consideration of techniques such as XAI to make the ML model's behavior traceable (Mohseni et al 2021), resulting in the need of EIS applications (Herm, Heinrich, et al, 2022a).…”

“…It is worth noting that the perceived cognitive load may vary by an individual due to context-specific circumstances (Oviatt 2006). Hence, EIS must provide accounts in a manner that reduces the cognitive effort of users (Zschech et al 2020).…”

Design Features and User Perspectives of Patient Confidentiality and Consent Features for Substance Use Disorder

“…The authors worked together with practitioners to iteratively design, develop, and implement a private, confidential, and secure software application and information sharing process for SUD transitions of care. The overall design requirements, principles, and solution approach is aligned with that of Peffers, et al which describes a six-step approach: 1) problem identification and motivation, 2) defining objective of the solution, 3) artifact design and development, 4) demonstration, 5) evaluation, and 6) communication (Peffers, Tuunanen, Rothenberger, & Chatterjee, 2007;Zschech, Horn, Höschele, Janiesch, & Heinrich, 2020). (See Figure 1)…”

Enhancing Learning Object Analysis through Fuzzy C-Means Clustering and Web Mining Methods

“…The fundamental objectives of web data mining can be summarized as follows: they bring invisible information to the fore, they take into account the volume of web data, they transform the massive amount of web data into expert knowledge, and they provide valuable knowledge to the users despite the numerous attempts to characterize this field. The term "web data mining process" is frequently used with a combination of different techniques from various disciplines, including data analysis, artificial intelligence, and machine learning [9]. A typical process of web data mining can be described in three successive steps: data preparation, or pre-processing data; discovering patterns; and analyzing patterns.…”