Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-12-13 Cooperative journals: 《计算机应用研究》
Abstract： 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 includes 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.
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