Deep mining of elderly health data based on improved association clustering

To deeply process the health data of the elderly, this paper designs a deep mining method for elderly health data based on an improved association clustering approach. Initially, health data samples from the elderly are collected. The Apriori algorithm is enhanced with interest constraints, connectivity operations are employed to generate candidate itemsets, and those that do not meet the requirements are eliminated. Associated feature quantities are then extracted from the health data. Subsequently, a fuzzy K-means algorithm with weight attributes is incorporated as the core method, and a balance coefficient is calculated using the principle of balanced contribution. Finally, the improved fuzzy K-means algorithm is utilised to complete data classification, detect abnormal data points, and achieve deep mining of the health data. The results indicate that the proposed method has a false alarm rate of less than 3.21% and a false negative rate of less than 1.81%, demonstrating a superior mining effect compared to the comparison method.