Revealing cluster formation over huge volatile robotic data

Nikos Mitsou, Irene Ntoutsi, Dirk Wollherr, Costas Tzafestas, Hans Peter Kriegel

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

2 Zitate (Scopus)

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.

OriginalspracheEnglisch
TitelProceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Seiten450-457
Seitenumfang8
DOIs
PublikationsstatusVeröffentlicht - 2011
Veranstaltung11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Kanada
Dauer: 11 Dez. 201111 Dez. 2011

Publikationsreihe

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Konferenz

Konferenz11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Land/GebietKanada
OrtVancouver, BC
Zeitraum11/12/1111/12/11

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