Proportionate feature selection - A pre-processing step for clustering

Accuracy and efficiency of clustering algorithms depend greatly on the input data. Thus, removing unimportant features from the dataset can help us form better clusters in lesser time. These unimportant features may be those that are redundant, or affected by noise, etc. We also need to consider the...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Sekhon, J.S., Gopalkrishnan, V., Keong, N.W.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Clustering Clustering algorithms Computational complexity Data analysis Data mining Distance Measure Feature Selection Filters Gene expression Humans Microarray Data Proportionate Number of Genes Similarity Measure Training data
Online-Zugang:	Volltext bestellen
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