Data Mining of Factors Associated with Sleep Quality of Anger-out and Anger-in Population Based on FP-Tree Growing Algorithm / 世界科学技术-中医药现代化

ABSTRACT

This article was aimed to study the factors associated with sleep quality of anger-out and anger-in population based on the frequent pattern-tree (FP-Tree) growing algorithm with data mining. The algorithm of structuring frequent model FP-tree and mining frequent itemsets were designed. The database information scanned was recorded by using FP-Tree growing algorithm through state-trees. The frequent itemsets met minimum support required was generated through reducing the search space of project sets and scanning database only one. The data mining of all factors associated with emotional diseases was actualized. The results showed that factors associated with sleep quality of anger-out and anger-in population were disturbance in respiration, cough or snoring, feeling cold, hot or nightmares. The total time for program analysis was 2 seconds. It was concluded that data mining algorithm based on FP-Tree frequent itemsets can effectively realize the useful information receiving from factors associated with emotional diseases.