An approach to outlier detection of software measurement data using the k-means clustering method

Proceedings - 1st International Symposium on Empirical Software Engineering and Measurement, ESEM 2007

Volumn , Issue , 2007, Pages 443-445

  Yoon, Kyung A ^a   Kwon, Oh Sung ^a   Bae, Doo Hwan ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER ANALYSIS; DECISION MAKING; ELECTRIC MEASURING INSTRUMENTS; FLOW OF SOLIDS; MATHEMATICAL MODELS; PROBLEM SOLVING; PROJECT MANAGEMENT; SOFTWARE ENGINEERING; TECHNOLOGY; WATER SUPPLY SYSTEMS;

DATA QUALITY; EMPIRICAL SOFTWARE ENGINEERING; INTERNATIONAL SYMPOSIUM; K-MEANS CLUSTERING METHODS; OUTLIER DETECTION; PREDICTION MODELING; PROJECT MANAGER; QUALITY OF SOFTWARES; SOFTWARE MEASUREMENTS;

COMPUTER SOFTWARE SELECTION AND EVALUATION;

EID: 47949101596     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ESEM.2007.16     Document Type: Conference Paper

References (5)

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2. Victoria J. Hodge and Jim Austin, "A Survey of Outlier Detection Methodologies", Artificial Intelligence Review pp.85-126, 2004
3. Paolo Giudici, Applied Data Mining: Statistical Methods for Business and Industry, Wiley, 2003
4. Ingunn Myrtveit, Erik Stensrud, and Ulf H. Olsson, "Analyzing Data Sets with Missing Data: An Empirical Evaluation of Imputation Methods and Likelihood-Based Methods", IEEE Transaction on Software Engineering, v.27 no. 11 pp.999-1013, 2001
5. Warren Sarle, "Cubic Clustering Criterion", SAS(R) Technical Report A-108, SAS Institute Inc., 1983 Wiley, 1987