Research and Application of Community Population Information Association Model Based on IoT Multi-device Mining
| Autor: | Junwei Geng, Lin Qin |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
education.field_of_study
Data collection Association rule learning business.industry Computer science Association (object-oriented programming) Reliability (computer networking) Association model Population Big data 02 engineering and technology computer.software_genre 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining education business Internet of Things computer |
| Zdroj: | BDIOT |
| DOI: | 10.1145/3361758.3361767 |
| Popis: | With the rapid development of big data and Internet of Things technologies, the amount of population information data has exploded, resulting in a series of research and application of population information data. However, the population information data is large and complex, so traditional statistical methods are too time-consuming and labor-intensive to meet data collection. This paper proposes a community population information association method based on IoT data mining. The method diversifies the population information data collected by multiple devices and makes appropriate adjustments to classic FP-Growth algorithm, which constructs a community population information association model based on improved FP-Growth algorithm and finally mines the corresponding association rules. The experimental results show that the association model can support the operation of big data in speed and memory. Furthermore, the mined association rules have been verified to have certain reliability. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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