&lt;title&gt;Adaptive clustering for hyperspectral sounder data compression&lt;/title&gt;

Gladkova, Irina; Roytman, Leonid; Goldberg, Mitchell D.

doi:10.1117/12.618085

Cited by 4 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, clustering is commonly used as an auxiliary task in many fields, e.g. wireless sensor networks [17], speech recognition [21], stochastic optimization [14], data compression [9], document organization, etc.…”

Section: Introductionmentioning

confidence: 99%

CICE-BCubed: A New Evaluation Measure for Overlapping Clustering Algorithms

Rosales-Méndez

Ramírez-Cruz

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

Abstract. The evaluation of clustering algorithms is a field of PatternRecognition still open to extensive debate. Most quality measures found in the literature have been conceived to evaluate non-overlapping clusterings, even when most real-life problems are better modeled using overlapping clustering algorithms. A number of desirable conditions to be satisfied by quality measures used to evaluate clustering algorithms have been proposed, but measures fulfilling all conditions still fail to adequately handle several phenomena arising in overlapping clustering. In this paper, we focus on a particular case of such desirable conditions, which existing measures that fulfill previously enunciated conditions fail to satisfy. We propose a new evaluation measure that correctly handles the studied phenomenon for the case of overlapping clusterings, while still satisfying the previously existing conditions.

show abstract

Section: Introductionmentioning

confidence: 99%