Developing a Decision Tree-Based Data Mining Model for Marketing in the Biotechnology Sector

Abstract

Addressing the challenges of low precision and lengthy processing times in existing marketing data mining approaches, this paper introduces a refined model specifically designed for the biotechnology sector, utilizing a decision tree algorithm. The methodology begins with an overview of the decision tree algorithm, including its generation and pruning processes. It incorporates clustering algorithms from data mining technology to categorize marketing data effectively, thereby enhancing the feature classification process. Using higher-order statistics, the model further refines its capability to extract significant features from compensation parameters, optimizing the feature extraction process for marketing data. These features are then segmented into subsets, and a comprehensive marketing data mining model is constructed based on the decision tree framework. This model is tailored to meet the unique needs of the biotechnology market, where precise and efficient data analysis is paramount. Experimental results confirm that the proposed model achieves high accuracy in extracting and analyzing marketing data and significantly reduces the time required for data mining. This advancement holds considerable promise for enhancing strategic marketing decisions within the commercial biotechnology industry, enabling firms to respond more swiftly and effectively to market dynamics.