The database BreastCancer_Wisconsin:

.. _breast_cancer_dataset: Breast cancer wisconsin (diagnostic) dataset -------------------------------------------- **Data Set Characteristics:**     :Number of Instances: 569     :Number of Attributes: 30 numeric, predictive attributes and the class     :Attribute Information:        - radius (mean of distances from center to points on the perimeter)        - texture (standard deviation of gray-scale values)        - perimeter        - area        - smoothness (local variation in radius lengths)        - compactness (perimeter^2 / area - 1.0)        - concavity (severity of concave portions of the contour)        - concave points (number of concave portions of the contour)        - symmetry        - fractal dimension ("coastline approximation" - 1)         The mean, standard error, and "worst" or largest (mean of the three        worst/largest values) of these features were computed for each image,        resulting in 30 features.  For instance, field 0 is Mean Radius, field        10 is Radius SE, field 20 is Worst Radius.         - class:                - WDBC-Malignant                - WDBC-Benign     :Summary Statistics:     ===================================== ====== ======                                           Min    Max    ===================================== ====== ======    radius (mean):                        6.981  28.11    texture (mean):                       9.71   39.28    perimeter (mean):                     43.79  188.5    area (mean):                          143.5  2501.0    smoothness (mean):                    0.053  0.163    compactness (mean):                   0.019  0.345    concavity (mean):                     0.0    0.427    concave points (mean):                0.0    0.201    symmetry (mean):                      0.106  0.304    fractal dimension (mean):             0.05   0.097    radius (standard error):              0.112  2.873    texture (standard error):             0.36   4.885    perimeter (standard error):           0.757  21.98    area (standard error):                6.802  542.2    smoothness (standard error):          0.002  0.031    compactness (standard error):         0.002  0.135    concavity (standard error):           0.0    0.396    concave points (standard error):      0.0    0.053    symmetry (standard error):            0.008  0.079    fractal dimension (standard error):   0.001  0.03    radius (worst):                       7.93   36.04    texture (worst):                      12.02  49.54    perimeter (worst):                    50.41  251.2    area (worst):                         185.2  4254.0    smoothness (worst):                   0.071  0.223    compactness (worst):                  0.027  1.058    concavity (worst):                    0.0    1.252    concave points (worst):               0.0    0.291    symmetry (worst):                     0.156  0.664    fractal dimension (worst):            0.055  0.208    ===================================== ====== ======     :Missing Attribute Values: None     :Class Distribution: 212 - Malignant, 357 - Benign     :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian     :Donor: Nick Street     :Date: November, 1995 This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets. https://goo.gl/U2Uwz2 Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass.  They describe characteristics of the cell nuclei present in the image. Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree.  Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes. The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34]. This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/ .. topic:: References    - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction      for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on      Electronic Imaging: Science and Technology, volume 1905, pages 861-870,     San Jose, CA, 1993.   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and      prognosis via linear programming. Operations Research, 43(4), pages 570-577,      July-August 1995.   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994)      163-171.

 

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Homework 2: Using the database (a) Apply Hierarchical Clustering with different linkage criteria (Ward, Maximum or complete linkage, Average linkage, Single linkage) and discuss the results obtained. BreastCancer_Wisconsin: (b) Apply the k-Means clustering algorithm with parameters optimization and evaluate the performance, discuss the results obtained. Homework 2: Using the database (a) Apply Hierarchical Clustering with different linkage criteria (Ward, Maximum or complete linkage, Average linkage, Single linkage) and discuss the results obtained. BreastCancer_Wisconsin: (b) Apply the k-Means clustering algorithm with parameters optimization and evaluate the performance, discuss the results obtained.

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a Hierarchical Clustering with different linkage criteria Hierarchical clustering is a method that builds a hierarchy of clusters by iteratively mergi... View the full answer