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|Title:||Semi-supervised clustering algorithms for web documents||Authors:||Bian, Zhiwei.||Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems||Issue Date:||2011||Abstract:||Data mining has been a significant tool in extracting hidden and useful information from large databases in various scientific and practical applications. One of the techniques is semi-supervised clustering. Semi-supervised algorithms often demonstrate surprisingly impressive performance improvements over traditional one-sided row clustering techniques by attempting to simultaneously partition both the rows and columns. In many application algorithms, partial supervision in the form of a few rows labeling information as well columns may be available to potentially increase the performance of semi-supervised clustering. In Sindhwani‟s paper, they proposed two novel semi-supervised clustering algorithms motivated respectively by spectral bipartite graph partitioning and matrix approximation formulations for co-clustering.||URI:||http://hdl.handle.net/10356/45760||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Student Reports (FYP/IA/PA/PI)|
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