A comparative study of dimensionality reduction techniques to enhance trace clustering performances

Expert Systems with Applications

Volumn 40, Issue 9, 2013, Pages 3722-3737

  Song, M ^a   Yang, H ^a   Siadat, S H ^a   Pechenizkiy, M ^b  

^a Ulsan National Institute of Science and Technology   (South Korea)

^b EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

PCA; Process mining; Random projection; Singular value decomposition; Trace clustering

Indexed keywords

BIG DATUM; COMPARATIVE STUDIES; COMPUTATION TIME; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION TECHNIQUES; DUTCH HOSPITALS; EVENT LOGS; EXPERIMENTAL STUDIES; FEATURE SPACE; FEATURE TRANSFORMATIONS; HIGH DIMENSIONALITY; IN-PROCESS; PCA; PRINCIPAL COMPONENTS ANALYSIS; PROCESS MINING; PROCESS MODEL; RANDOM PROJECTIONS; STATE OF THE ART; TRACE CLUSTERING; TREATMENT PROCESS; USEFUL PATTERNS;

DATA MINING; PATIENT TREATMENT; PRINCIPAL COMPONENT ANALYSIS; SINGULAR VALUE DECOMPOSITION;

TRACE ANALYSIS;

EID: 84874651427     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.12.078     Document Type: Article

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