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+## About Dataset
+### Relevant information:
+
+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].
+
+Number of instances: 569
+
+Number of attributes: 32 (ID, diagnosis, 30 real-valued input features)
+
+Diagnosis (M = malignant, B = benign)
+
+Ten real-valued features are computed for each cell nucleus:
+
+a) radius (mean of distances from center to points on the perimeter)
+b) texture (standard deviation of gray-scale values)
+c) perimeter
+d) area
+e) smoothness (local variation in radius lengths)
+f) compactness (perimeter^2 / area - 1.0)
+g) concavity (severity of concave portions of the contour)
+h) concave points (number of concave portions of the contour)
+i) symmetry
+j) fractal dimension ("coastline approximation" - 1)
+
+Missing attribute values: none
+
+Class distribution: 357 benign, 212 malignant
+
+### Creators:
+
+Dr. William H. Wolberg, General Surgery Dept., University of Wisconsin.
+
+W. Nick Street, Computer Sciences Dept., University of Wisconsin.
+
+Olvi L. Mangasarian, Computer Sciences Dept., University of Wisconsin.
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