基于FP-Growth的智能家居用户时序关联操控习惯挖掘方法

针对传统关联规则挖掘算法无法高效且准确地挖掘出隐含于用户操作记录中的时序关联操控习惯，提出一种基于FP-Growth的智能家居用户时序关联操控习惯挖掘算法。该算法分为三个阶段，分别为基于用户操控动作森林、改进的FP-Growth算法和一种时间约束规则进行事务集的生成、时序频繁项集的生成以及最终时序关联操控习惯的生成。最后，使用真实用户操控记录进行对比实验，结果表明该算法能提高生成事务集的效率，并能更准确地发现用户操控家居设备的时序关联习惯。

FP-Growth-based user temporal association control habits mining method for smart home

Concern the problem that the traditional association analysis algorithms cannot efficiently and accurately mine the user''s potential temporal association control habits which are implied in the user''s operation records, this paper proposed a novel user temporal association control habits mining method based on FP-Growth. This method included three stages: to generate the transaction set, the temporal frequent item set, and the final temporal association control habits via the user operation-action forest, the improved FP-Growth algorithm and a time constraint rule. Finally, the comparative experiments by using the real user control records show that this method can improve the efficiency of transaction set generation and can more accurately discover the user''s temporal association habits of smart home devices.