Towards mobility-based clustering

Identifying hot spots of moving vehicles in an urban area is essential to many smart city applications. The practical research on hot spots in smart city presents many unique features, such as highly mobile environments, supremely limited size of sample objects, and the non-uniform, biased samples. All these features have raised new challenges that make the traditional density-based clustering algorithms fail to capture the real clustering property of objects, making the results less meaningful. In this paper we propose a novel, non-density-based approach called mobility-based clustering. The key idea is that sample objects are employed as “sensors” to perceive the vehicle crowdedness in nearby areas using their instant mobility, rather than the “object representatives”. As such the mobility of samples is naturally incorporated. Several key factors beyond the vehicle crowdedness have been identified and techniques to compensate these effects are proposed. We evaluate the perf...

• Type: Journal
• Year: 2010
• Where: KDD
• Authors: Siyuan Liu, Yunhuai Liu, Lionel M. Ni, Jianping Fan 0002, Minglu Li
• Keywords: Data Mining, Hot Spots, KDD 2010, Sample Objects, Smart City