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

Abstract

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.

Critical Review of Neural Network Generations and Models Design

“…ANNs learn from raw data by adjusting the weights of node connections of complex datasets, resulting in more appropriate figures, which allows them to extract complex patterns and relationships. The field 2 of ANNs has seen several generations of development, each characterized by a distinct set of architectures and algorithms that enable them to perform increasingly complex tasks [6,7]. The firstgeneration ANNs, which emerged in the 1950s and 1960s, were relatively simple models with only a few layers of neurons, mainly used for pattern classification and regression tasks [8,9].…”