Rek-means: a k-means based clustering algorithm

Authors:
Domenico Daniele Bloisi;Luca Iocchi
Affiliations:
Sapienza University of Rome, Dipartimento di Informatica e Sistemistica, Rome, Italy;Sapienza University of Rome, Dipartimento di Informatica e Sistemistica, Rome, Italy
Venue:
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Year:
2008

Citing 3
Cited 1

Data clustering: a review

ACM Computing Surveys (CSUR)
How slow is the k-means method?

Proceedings of the twenty-second annual symposium on Computational geometry
Can exclusive clustering on streaming data be achieved?

ACM SIGKDD Explorations Newsletter

An Adaptive Tracker for Assisted Living

AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a new clustering method based on k-means that has been implemented on a video surveillance system. Rek-means does not require to specify in advance the number of clusters to search for and is more precise than k-means in clustering data coming from multiple Gaussian distributions with different co-variances, while maintaining real-time performance. Experiments on real and synthetic datasets are presented to measure the effectiveness and the performance of the proposed method.