Exact and Approximation Algorithms for Clustering

Agarwal, Pankaj K.; Procopiuc, Cecilia M.

doi:10.1007/s00453-001-0110-y

Cited by 226 publications

(214 citation statements)

References 9 publications

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“…LEACH-C [1] is a variation that uses a centralized clustering algorithm at the sink based on simulated annealing to determine k optimal clusters. This is an NP hard problem [9]. HEED [3] is a probabilistic method that determines the probability using the residual energy of each node.…”

Section: Previous Workmentioning

confidence: 99%

Cluster head selection using RF signal strength

Fazackerley

Paeth

Lawrence

2009

2009 Canadian Conference on Electrical and Computer Engineering

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The LEACH algorithm for selecting cluster heads is a probabilistic method which produces clusters with a large variation of link distances and uneven energy consumption during the data transmission phase. To address this issue, a RF signal strength algorithm based on link quality is presented. Using a competitive distributed algorithm, nodes attempt to reduce the overall energy required for transmission in addition to forming favourable clusters based on Received Signal Strength Indication (RSSI) density and quality. Cluster heads form in areas of high node density leading to a significant reduction in transmission link length, a reduced variance in link length distribution and greater opportunity for energy savings through data aggregation. Simulations show that cluster heads selected by this algorithm form clusters with a lower average link length and have less link distance variability. This produces a lower and more evenly distributed energy cost per node in the network.

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Section: Previous Workmentioning

confidence: 99%

Cluster head selection using RF signal strength

Fazackerley

Paeth

Lawrence

2009

2009 Canadian Conference on Electrical and Computer Engineering

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“…The first was the K-means, an unsupervised classifier commonly found in the literature [50], followed by an automated labeling of the clusters (Figure 4). K-means clustering consisted of minimizing the mean square d-dimensional distance between each pixel to its closest cluster center [51]. One hundred clusters were created, aggregating spectrally-close pixels.…”

Section: Classification Algorithmsmentioning

confidence: 99%

An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series

et al. 2015

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Cropland mapping relies heavily on field data for algorithm calibration, making it, in many cases, applicable only at the field campaign scale. While the recently launched Sentinel-2 satellite will be able to deliver time series over large regions, it will not really be compatible with the current mapping approach or the available in situ data. This research introduces a generic methodology for mapping annual cropland along the season at high spatial resolution with the use of globally available baseline land cover and no need for field data. The methodology is based on cropland-specific temporal features, which are able to cope with the diversity of agricultural systems, prior information from which mislabeled pixels have been removed and a cost-effective classifier. Thanks to the JECAM network, eight sites across the world were selected for global cropland mapping benchmarking. Accurate cropland maps were produced at the end of the season, showing an overall accuracy Remote Sens. 2015, 7 13209 of more than 85%. Early cropland maps were also obtained at three-month intervals after the beginning of the growing season, and these showed reasonable accuracy at the three-month stage (>70% overall accuracy) and progressive improvement along the season. The trimming-based method was found to be key for using spatially coarse baseline land cover information and, thus, avoiding costly field campaigns for prior information retrieval. The accuracy and timeliness of the proposed approach shows that it has substantial potential for operational agriculture monitoring programs.

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“…Our method is similar to techniques used for approximating the standard k-center problem (e.g. [2]). …”

Section: An Approximation Algorithmmentioning

confidence: 99%

Bichromatic 2-Center of Pairs of Points

Arkin

Díaz-Báñez

Hurtado

et al. 2012

LATIN 2012: Theoretical Informatics

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Abstract. We study a class of geometric optimization problems closely related to the 2-center problem: Given a set S of n pairs of points, assign to each point a color ("red" or "blue") so that each pair's points are assigned different colors and a function of the radii of the minimum enclosing balls of the red points and the blue points, respectively, is optimized. In particular, we consider the problems of minimizing the maximum and minimizing the sum of the two radii. For each case, minmax and minsum, we consider distances measured in the L2 and in the L∞ metrics. Our problems are motivated by a facility location problem in transportation system design, in which we are given origin/destination pairs of points for desired travel, and our goal is to locate an optimal road/flight segment in order to minimize the travel to/from the endpoints of the segment.

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Exact and Approximation Algorithms for Clustering

Cited by 226 publications

References 9 publications

Cluster head selection using RF signal strength

Cluster head selection using RF signal strength

An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series

Bichromatic 2-Center of Pairs of Points

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