Domain Knowledge Transfer for Feature Exploration

Transferring domain knowledge is crucial for effective feature exploration in data analysis and machine learning. Given the extensive amount of data available today, no dataset remains entirely novel within its domain. However, our approach to handling datasets by segregating them from domain knowledge may lead us to overlook insights that could significantly enhance the process of comprehending the data. When delving into familiar knowledge using novel datasets, there's a risk of duplicating efforts and wasting resources and time. To address this, we introduce an implementation strategy aimed at incorporating domain knowledge into a fresh dataset. This strategy involves categorizing the attributes within the dataset into classes. By analyzing the class counts for each attribute, we propose generating associative rules. These rules can then be integrated into the new dataset as additional attributes, effectively infusing it with domain knowledge.