Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Machine Learning
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
An integrated method of adaptive enhancement for unsupervised segmentation of MRI brain images
Pattern Recognition Letters
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
Expert Systems with Applications: An International Journal
ESTDD: Expert system for thyroid diseases diagnosis
Expert Systems with Applications: An International Journal
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Classification method using fuzzy level set subgrouping
Expert Systems with Applications: An International Journal
Feature Extraction for Dynamic Integration of Classifiers
Fundamenta Informaticae
A comparative study on thyroid disease diagnosis using neural networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Cluster Analysis
Robust cluster validity indexes
Pattern Recognition
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
An automatic diagnosis system based on thyroid gland: ADSTG
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Variance enhanced K-medoid clustering
Expert Systems with Applications: An International Journal
Validity index for clusters of different sizes and densities
Pattern Recognition Letters
A Clustering Performance Measure Based on Fuzzy Set Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intuitionistic fuzzy MST clustering algorithms
Computers and Industrial Engineering
Validity-guided (re)clustering with applications to image segmentation
IEEE Transactions on Fuzzy Systems
A Three-Stage Expert System Based on Support Vector Machines for Thyroid Disease Diagnosis
Journal of Medical Systems
A self-organizing network for hyperellipsoidal clustering (HEC)
IEEE Transactions on Neural Networks
A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine
Journal of Medical Systems
Fuzzy Kohonen clustering networks for interval data
Neurocomputing
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
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Thyroid hormones produced by the thyroid gland help regulation of the body's metabolism. A variety of methods have been proposed in the literature for thyroid disease classification. As far as we know, clustering techniques have not been used in thyroid diseases data set so far. This paper proposes a comparison between hard and fuzzy clustering algorithms for thyroid diseases data set in order to find the optimal number of clusters. Different scalar validity measures are used in comparing the performances of the proposed clustering systems. To demonstrate the performance of each algorithm, the feature values that represent thyroid disease are used as input for the system. Several runs are carried out and recorded with a different number of clusters being specified for each run (between 2 and 11), so as to establish the optimum number of clusters. To find the optimal number of clusters, the so-called elbow criterion is applied. The experimental results revealed that for all algorithms, the elbow was located at c=3. The clustering results for all algorithms are then visualized by the Sammon mapping method to find a low-dimensional (normally 2D or 3D) representation of a set of points distributed in a high dimensional pattern space. At the end of this study, some recommendations are formulated to improve determining the actual number of clusters present in the data set.