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ICPR
2008
IEEE
16 years 25 days ago
Incremental clustering via nonnegative matrix factorization
Nonnegative matrix factorization (NMF) has been shown to be an efficient clustering tool. However, NMF`s batch nature necessitates recomputation of whole basis set for new samples...
Serhat Selcuk Bucak, Bilge Günsel
121
Voted
WEBI
2007
Springer
16 years 14 days ago
K-SVMeans: A Hybrid Clustering Algorithm for Multi-Type Interrelated Datasets
Identification of distinct clusters of documents in text collections has traditionally been addressed by making the assumption that the data instances can only be represented by ...
Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee G...
ICDM
2003
IEEE
240views Data Mining» more  ICDM 2003»
15 years 11 months ago
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
Given the recent explosion of interest in streaming data and online algorithms, clustering of time series subsequences, extracted via a sliding window, has received much attention...
Eamonn J. Keogh, Jessica Lin, Wagner Truppel
COLT
2010
Springer
15 years 4 months ago
Robust Hierarchical Clustering
One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across many different fields ranging from computational...
Maria-Florina Balcan, Pramod Gupta
KDD
2012
ACM
281views Data Mining» more  KDD 2012»
13 years 8 months ago
Active spectral clustering via iterative uncertainty reduction
Spectral clustering is a widely used method for organizing data that only relies on pairwise similarity measurements. This makes its application to non-vectorial data straightforw...
Fabian L. Wauthier, Nebojsa Jojic, Michael I. Jord...