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BMCBI
2006
159views more  BMCBI 2006»
15 years 4 months ago
MultiSeq: unifying sequence and structure data for evolutionary analysis
Background: Since the publication of the first draft of the human genome in 2000, bioinformatic data have been accumulating at an overwhelming pace. Currently, more than 3 million...
Elijah Roberts, John Eargle, Dan Wright, Zaida Lut...
ESWS
2008
Springer
15 years 6 months ago
Conceptual Clustering and Its Application to Concept Drift and Novelty Detection
Abstract. The paper presents a clustering method which can be applied to populated ontologies for discovering interesting groupings of resources therein. The method exploits a simp...
Nicola Fanizzi, Claudia d'Amato, Floriana Esposito
CEC
2010
IEEE
15 years 2 months ago
Real-coded differential crisp clustering for MRI brain image segmentation
Abstract-- In this paper, a segmentation technique of multispectral magnetic resonance image of the brain using a new differential evolution based crisp clustering is proposed. Rea...
Indrajit Saha, Ujjwal Maulik, Sanghamitra Bandyopa...
123
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ICONIP
2004
15 years 5 months ago
Semi-supervised Kernel-Based Fuzzy C-Means
This paper presents a semi-supervised kernel-based fuzzy c-means algorithm called S2KFCM by introducing semi-supervised learning technique and the kernel method simultaneously into...
Daoqiang Zhang, Keren Tan, Songcan Chen
KDD
2007
ACM
159views Data Mining» more  KDD 2007»
16 years 4 months ago
Constraint-driven clustering
Clustering methods can be either data-driven or need-driven. Data-driven methods intend to discover the true structure of the underlying data while need-driven methods aims at org...
Rong Ge, Martin Ester, Wen Jin, Ian Davidson