Revealing cluster formation over huge volatile robotic data

Mitsou, N; Ntoutsi, I; Wollherr, D; Tzafestas, C; Kriegel, H-P

dc.contributor.author	Mitsou, N	en
dc.contributor.author	Ntoutsi, I	en
dc.contributor.author	Wollherr, D	en
dc.contributor.author	Tzafestas, C	en
dc.contributor.author	Kriegel, H-P	en
dc.date.accessioned	2014-03-01T02:53:27Z
dc.date.available	2014-03-01T02:53:27Z
dc.date.issued	2011	en
dc.identifier.issn	15504786	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36327
dc.subject	Cluster formation	en
dc.subject	Grid clustering	en
dc.subject	Robot data	en
dc.subject	Sensor data	en
dc.subject	Stream clustering	en
dc.subject.other	Cluster formations	en
dc.subject.other	Detecting objects	en
dc.subject.other	Field methods	en
dc.subject.other	Grid clustering	en
dc.subject.other	Grid structures	en
dc.subject.other	Grid-based algorithms	en
dc.subject.other	Partial observation	en
dc.subject.other	Sensor data	en
dc.subject.other	Stream clustering	en
dc.subject.other	Time points	en
dc.subject.other	Unknown environments	en
dc.subject.other	Data mining	en
dc.subject.other	Robots	en
dc.subject.other	Sensors	en
dc.subject.other	Robotics	en
dc.title	Revealing cluster formation over huge volatile robotic data	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICDMW.2011.147	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICDMW.2011.147	en
heal.identifier.secondary	6137414	en
heal.publicationDate	2011	en
heal.abstract	In this paper, we propose a driven by the robotics field method for revealing global clusters over a fast, huge and volatile stream of robotic data. The stream comes from a mobile robot which autonomously navigates in an unknown environment perceiving it through its sensors. The sensor data arrives fast, is huge and evolves quickly over time as the robot explores the environment and observes new objects or new parts of already observed objects. To deal with the nature of data, we propose a grid-based algorithm that updates the grid structure and adjusts the so far built clusters online. Our method is capable of detecting object formations over time based on the partial observations of the robot at each time point. Experiments on real data verify the usefulness and efficiency of our method. © 2011 IEEE.	en
heal.journalName	Proceedings - IEEE International Conference on Data Mining, ICDM	en
dc.identifier.doi	10.1109/ICDMW.2011.147	en
dc.identifier.spage	450	en
dc.identifier.epage	457	en