关键词: 数据挖掘, 关联规则, 关联规则压缩, 频繁项集, 焦虑, 抑郁

Abstract: For early prevention and diagnosis of patients with anxiety and depression, this paper applies association rule mining and summarization methods to medical records to discover sets of risk factors associated with anxiety and depression. Separate use of frequent itemsets mining algorithm would produce too many frequent itemsets and association rules, causing its practicability greatly reduced. It preprocesses the medical records. Then it uses the FP-growth algorithm to find frequent itemsets in the data after pretreatment. At last, it uses the latest improvement Bottom-Up Summarization（BUS） algorithm to summarize the discovered frequent itemsets. At the same time, it compares the association rules obtained at last with the association rules uncompressed and the association rules obtained by the original BUS algorithm and Top-K. Experimental results show that the rules obtained by improved BUS algorithm have moderate number, less redundant information and the people covered by these rules are at high risk of anxiety or depression.

Key words: data mining, association rules, association rule summarization, frequent itemsets, anxiety, depression