Multi-dimensional features models and compacted clustering for ILBD (Indoor Location Big Data)

作者：Lin, Wenliang^*; Deng, Zhongliang; Li, Xueming; Fang, Qin; Li, Ning; Wang, Ke

来源：Journal of Intelligent and Fuzzy Systems, 2017, 33(5): 2811-2822.

DOI：10.3233/JIFS-169330

• LBS
• ILBD
• data process
• clustering and mining
• data models

LBS (Location Based Services) have been a type of "killer application" for ongoing and upcoming internet services. ILBD (Indoor Location Big data) are extremely big multimedia data indeed. However, indoor location data are more complicated than outdoor. Lack of unified representation model and data redundancies make ILBD hard to cluster and mine location based values. Therefore, this paper proposes a new multi-dimensional features model and compacted clustering for ILBD. Unified ILBD model combines spatial and time features of different scales and states, which employs normalized data frames to pre-process original data. Scalable Euclidean extending distance is designed to characterize relationships between heterogeneous data and represent connection of different dimensions. In order to reduce ILBD redundancies and flaws, compacted clustering method are proposed, which construct location ontology and sensations parameters to determine ILBD main affecting elements, the sluggish elements would be filtered and shrink to decrease the amount of ILBD. The new multi-dimensional features model would be applied in LBS framework. The tests and simulations verify proposed model have enhanced 36.7% convergence estimation RMSE and 12.3% regional flow estimation accuracy performance, which improve accuracy of ILBD mining and reduce ILBD redundancies and flaws.

• 出版日期2017
• 单位北京邮电大学