# How to calculate proximity measure forÂ asymmetric binary attributes?

In this tutorial, we will learn about the proximity measure forÂ asymmetric binary attributes

## Contingency table for binary data

Here in this example, consider 1 for positive/True Â and 0 for negative/False.

Object 2 |
||||

Object 1 |
Â | 1 / True / Positive | 0 / False / Negative | Sum |

1 /Â True / Positive | A | B | A + B | |

0 / False / Negative | C | D | C + D | |

Sum | A + C | B + D | Â |

In table 1 we can consider the following facts.

A represents that object 1 is True and object 2 is also True.

B represents that object 1 is True and object 2 is also False.

C represents that object 1 is False and object 2 is also True.

D represents that object 1 is False and object 2 is also False.

Name |
Fever |
Cough |
Test 1 |
Test 2 |
Test 3 |
Test 4 |

Asad | Negative | Yes | Â Negative | Â Positive | Â Negative | Â Negative |

Bilal | Negative | Â Yes | Â Negative | Positive | Â Positive | Â Negative |

Tahir | Positive | Yes | Â Negative | Â Negative | Â Negative | Â Negative |

In table 2, Asad, Bilal and Tahir are objects. Negative values represents False and Positive represents Negative.

In the results, we can see the following facts;

The distance between object 1 and 2 is 0.67. Asad is objectÂ 1 and Tahir is in object 2 and the distance between both is 0.67.

Less distance is between Asad and Bilal. It means that Asad and Bilal are more similar to each other as compared to other objects.

## Video Lecture

## Next Similar Tutorials

- Proximity Measure for Nominal Attributes – Click Here
- Distance measure for asymmetric binary attributes – Click Here
- Distance measure for symmetric binary variables – Click Here
- Euclidean distance in data mining – Click Here Euclidean distance Excel file – Click Here
- Jaccard coefficient similarity measure for asymmetric binary variables – Click Here