A similarity-based robust clustering method

This paper presents an alternating optimization clustering procedure called a similarity-based clustering method (SCM). It is an effective and robust approach to clustering on the basis of a total similarity objective function related to the approximate density shape estimation. We show that the dat...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 26; H. 4; S. 434 - 448
Hauptverfasser: Yang, Miin-Shen, Wu, Kuo-Lung
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.04.2004
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial Intelligence
Cluster Analysis
Clustering algorithms
Clustering methods
Computer Simulation
Data mining
Fuzzy Logic
Image Interpretation, Computer-Assisted - methods
Information Storage and Retrieval - methods
Mathematical models
Noise robustness
Noise shaping
Numerical Analysis, Computer-Assisted
Optimization methods
Pattern recognition
Pattern Recognition, Automated
Prototypes
Reproducibility of Results
Sensitivity analysis
Sensitivity and Specificity
Shape
Signal Processing, Computer-Assisted
Studies
Subtraction Technique
ISSN:0162-8828, 1939-3539
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Zusammenfassung:This paper presents an alternating optimization clustering procedure called a similarity-based clustering method (SCM). It is an effective and robust approach to clustering on the basis of a total similarity objective function related to the approximate density shape estimation. We show that the data points in SCM can self-organize local optimal cluster number and volumes without using cluster validity functions or a variance-covariance matrix. The proposed clustering method is also robust to noise and outliers based on the influence function and gross error sensitivity analysis. Therefore, SCM exhibits three robust clustering characteristics: 1) robust to the initialization (cluster number and initial guesses), 2) robust to cluster volumes (ability to detect different volumes of clusters), and 3) robust to noise and outliers. Several numerical data sets and actual data are used in the SCM to show these good aspects. The computational complexity of SCM is also analyzed. Some experimental results of comparing the proposed SCM with the existing methods show the superiority of the SCM method.
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ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2004.1265860