Data

seeds

active
ARFF
Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani

0 likes downloaded by 5 people , 7 total downloads 0 issues 0 downvotes

0 likes downloaded by 5 people , 7 total downloads 0 issues 0 downvotes

Issue | #Downvotes for this reason | By |
---|

Loading wiki

Help us complete this description
Edit

Author: M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik, S. Zak
Source: UCI
Please cite: Contributors gratefully acknowledge support of their work by the Institute of Agrophysics of the Polish Academy of Sciences in Lublin.
* Title:
seeds Data Set
* Abstract:
Measurements of geometrical properties of kernels belonging to three different varieties of wheat. A soft X-ray technique and GRAINS package were used to construct all seven, real-valued attributes.
* Source:
MaÅ‚gorzata Charytanowicz, Jerzy Niewczas
Institute of Mathematics and Computer Science,
The John Paul II Catholic University of Lublin, KonstantynÃ³w 1 H,
PL 20-708 Lublin, Poland
e-mail: {mchmat,jniewczas}@kul.lublin.pl
Piotr Kulczycki, Piotr A. Kowalski, Szymon Lukasik, Slawomir Zak
Department of Automatic Control and Information Technology,
Cracow University of Technology, Warszawska 24, PL 31-155 Cracow, Poland
and
Systems Research Institute, Polish Academy of Sciences, Newelska 6,
PL 01-447 Warsaw, Poland
e-mail: {kulczycki,pakowal,slukasik,slzak}@ibspan.waw.pl
* Data Set Information:
The examined group comprised kernels belonging to three different varieties of wheat: Kama, Rosa and Canadian, 70 elements each, randomly selected for the experiment. High quality visualization of the internal kernel structure was detected using a soft X-ray technique. It is non-destructive and considerably cheaper than other more sophisticated imaging techniques like scanning microscopy or laser technology. The images were recorded on 13x18 cm X-ray KODAK plates. Studies were conducted using combine harvested wheat grain originating from experimental fields, explored at the Institute of Agrophysics of the Polish Academy of Sciences in Lublin. The data set can be used for the tasks of classification and cluster analysis.
* Attribute Information:
To construct the data, seven geometric parameters of wheat kernels were measured:
1. area A,
2. perimeter P,
3. compactness C = 4*pi*A/P^2,
4. length of kernel,
5. width of kernel,
6. asymmetry coefficient
7. length of kernel groove.
All of these parameters were real-valued continuous.
* Relevant Papers:
M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik, S. Zak, 'A Complete Gradient Clustering Algorithm for Features Analysis of X-ray Images', in: Information Technologies in Biomedicine, Ewa Pietka, Jacek Kawa (eds.), Springer-Verlag, Berlin-Heidelberg, 2010, pp. 15-24.

Class (target) | nominal | 3 unique values 0 missing | |

V1 | numeric | 193 unique values 0 missing | |

V2 | numeric | 170 unique values 0 missing | |

V3 | numeric | 186 unique values 0 missing | |

V4 | numeric | 188 unique values 0 missing | |

V5 | numeric | 184 unique values 0 missing | |

V6 | numeric | 207 unique values 0 missing | |

V7 | numeric | 148 unique values 0 missing |

0.49

Second quartile (Median) of standard deviation of attributes of the numeric type.

-0.14

Third quartile of kurtosis among attributes of the numeric type.

Minimal mutual information between the nominal attributes and the target attribute.

Maximum mutual information between the nominal attributes and the target attribute.

3

The minimal number of distinct values among attributes of the nominal type.

Third quartile of mutual information between the nominal attributes and the target attribute.

3

The maximum number of distinct values among attributes of the nominal type.

0.53

Third quartile of skewness among attributes of the numeric type.

-1.1

First quartile of kurtosis among attributes of the numeric type.

1.5

Third quartile of standard deviation of attributes of the numeric type.

0

Standard deviation of the number of distinct values among attributes of the nominal type.

First quartile of mutual information between the nominal attributes and the target attribute.

0.13

First quartile of skewness among attributes of the numeric type.

0.38

First quartile of standard deviation of attributes of the numeric type.

Average mutual information between the nominal attributes and the target attribute.

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

-0.84

Second quartile (Median) of kurtosis among attributes of the numeric type.

3

Average number of distinct values among the attributes of the nominal type.

5.41

Second quartile (Median) of means among attributes of the numeric type.

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

0.4

Second quartile (Median) of skewness among attributes of the numeric type.