A Paradigm-Shifting From Domain-Driven Data Mining Frameworks to Process-Based Domain-Driven Data Mining-Actionable Knowledge Discovery Framework

Fatima, Fakeeha; Talib, Ramzan; Hanif, Muhammad Kashif; Awais, Muhammad

doi:10.1109/access.2020.3039111

Cited by 13 publications

(7 citation statements)

References 32 publications

(38 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The case study results were evaluated and validated from different real-life domains scenarios such as engineering, education, and business process domains. Results showed that the actionability of learned rules was improved when considering process relevant factors from the above five perspectives of a business process compared to the rules learned from dataset or domain knowledge [48].…”

Section: Vvijay1 and Msatyanarayana (2012) Proposed An Actionable Ass...mentioning

confidence: 99%

Domain-Driven Actionable Knowledge Discovery for Traffic Accidents Using Rules Induction

Yousif¹,

Agarwal²,

Pareek³

2022

IJRST

View full text Add to dashboard Cite

Due to the limitation of the methodologies of traditional data mining to satisfy business expectations, the shift from mining data-centered hidden patterns to domain-driven actionable knowledge discovery has become a significant direction of KDD research [22]. Traditional data mining algorithms and tools face major obstacles and challenges to solve real-life business problems and issues as they fail to provide actions that can be taken by people in business based on generated rules [22]. A small set of rules are generated by standard classification algorithms to form a classifier, but these classification algorithms use domain independent biases and heuristics [2]. This research aimed to propose a new approach to find actionable rules from sets of discovered rules. It focused on how a combination of traditional classification data mining and domain-driven data mining approach could be applied in solving real-life problems related to the field of traffic accidents in UAE. Real-life data were collected and pre-processed using the user’s existing knowledge and needs. Classification using Rules Induction was applied on the domain-driven dataset. The discovered rules from this technique were then summarized, combined, and analyzed. The final set of actionable rules from Classification technique for each class was then generated using a proposed interestingness method. To support such a process, the domain driven in-depth pattern discovery (DDID-PK) framework was followed [9]. Based on experimental results, the extracted domain-driven rules were more interesting and actionable than those produced by the traditional classification technique of data mining. In addition, the integration of data-centered classification technique of data mining to domain-driven approach of data mining and actionable knowledge discovery could help the Dubai police authority to reduce traffic accident severity by formulating new policies and traffic rules based on the domain-driven knowledge extracted from some hidden patterns from real data.

show abstract

Section: Vvijay1 and Msatyanarayana (2012) Proposed An Actionable Ass...mentioning

confidence: 99%

Domain-Driven Actionable Knowledge Discovery for Traffic Accidents Using Rules Induction

Yousif¹,

Agarwal²,

Pareek³

2022

IJRST

View full text Add to dashboard Cite

show abstract

“…The work mentioned in [12] proposed a domain driven data mining approach for developing an actionable knowledge discovery framework. The primary objective of this research is to enhance the implementation of the learned rules.…”

Section: Literature Reviewmentioning

confidence: 99%

A combination of SEMMA & CRISP-DM models for effectively handling big data using formal concept analysis based knowledge discovery: A data mining approach

Firas¹

2023

World J. Adv. Eng. Technol. Sci.

View full text Add to dashboard Cite

Data analytics has emerged as one of the most advanced technologies in recent times. However, the successful implementation of analytics is still a great challenge since they suffer from technical barriers and have a lack of structured approaches for performing analytics. Data mining models are considered as a potential tool for solving problems related to data analytics. Data mining is a process used for extracting the relevant attributes from raw data, which is further processed using the mechanism of knowledge discovery for support decision making. Formal concept analysis (FCA) provides a robust platform for knowledge discovery and helps in the successful adoption of data mining for handling big data. Several mining techniques powered by FCA are discussed by the researchers. However, the analysis of FCA suggests that the effectiveness of FCA for big data needs, a deeper investigation in order to expand its application horizon. In this context, this research emphasizes the application of FCA for developing an effective strategy through a combination of SEMMA and CRISP models for handling big data by integrating knowledge discovery with data mining.

show abstract

“…Data in healthcare collected from different devices and sensors and their integration has become a challenging issue [32]. Fusion approaches should be capable of dynamically combine data belonging to multiple formats and sources [69]. Although, some fusion approaches have been proposed in the literature to manage continuous streaming data.…”

Section: B Management Of Streaming Datamentioning

confidence: 99%

Performance Enhancement of Predictive Analytics for Health Informatics Using Dimensionality Reduction Techniques and Fusion Frameworks

2022

Self Cite

View full text Add to dashboard Cite

Predictive analytics has become an essential area of research in health informatics. The availability of multi-source and multi-modal data in healthcare has made the disease prediction, diagnosis, and medication process more effective and reliable. However, the analysis and decision making have become challenging task, particularly when data is in multiple formats and from different sources. In this study, different frameworks have been proposed to handle multi-nature data at different levels for predictive analytics. Dimensionality reduction techniques have been applied to extract relevant features to enhance the analysis. To improve the performance of predictive analytics at different fusion levels, the potential benefits of multi-modal data have been discussed. Moreover, notable improvement in prediction accuracy has been observed through experimental evaluation of the proposed frameworks. Furthermore, the issues which have been found during dimension reduction and fusion approaches have also been highlighted.

show abstract

A Paradigm-Shifting From Domain-Driven Data Mining Frameworks to Process-Based Domain-Driven Data Mining-Actionable Knowledge Discovery Framework

Cited by 13 publications

References 32 publications

Domain-Driven Actionable Knowledge Discovery for Traffic Accidents Using Rules Induction

Domain-Driven Actionable Knowledge Discovery for Traffic Accidents Using Rules Induction

A combination of SEMMA & CRISP-DM models for effectively handling big data using formal concept analysis based knowledge discovery: A data mining approach

Performance Enhancement of Predictive Analytics for Health Informatics Using Dimensionality Reduction Techniques and Fusion Frameworks

Contact Info

Product

Resources

About