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ICDM
2009
IEEE
176views Data Mining» more  ICDM 2009»
14 years 11 months ago
SISC: A Text Classification Approach Using Semi Supervised Subspace Clustering
Text classification poses some specific challenges. One such challenge is its high dimensionality where each document (data point) contains only a small subset of them. In this pap...
Mohammad Salim Ahmed, Latifur Khan
JCP
2008
133views more  JCP 2008»
15 years 1 months ago
DDSC : A Density Differentiated Spatial Clustering Technique
Finding clusters with widely differing sizes, shapes and densities in presence of noise and outliers is a challenging job. The DBSCAN is a versatile clustering algorithm that can f...
Bhogeswar Borah, Dhruba K. Bhattacharyya
MM
2003
ACM
84views Multimedia» more  MM 2003»
15 years 6 months ago
Temporal event clustering for digital photo collections
We present similarity-based methods to cluster digital photos by time and image content. The approach is general, unsupervised, and makes minimal assumptions regarding the structu...
Matthew L. Cooper, Jonathan Foote, Andreas Girgens...
FUIN
2011
358views Cryptology» more  FUIN 2011»
14 years 5 months ago
Unsupervised and Supervised Learning Approaches Together for Microarray Analysis
In this article, a novel concept is introduced by using both unsupervised and supervised learning. For unsupervised learning, the problem of fuzzy clustering in microarray data as ...
Indrajit Saha, Ujjwal Maulik, Sanghamitra Bandyopa...
KDD
2007
ACM
159views Data Mining» more  KDD 2007»
16 years 1 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