# DataGenX

My e-Notes about DataScience, Machine Learning, Python, Data Analytics, DataStage, DWH and ETL Concepts

## Monday, 8 May 2017

Yet another question is in data mining to measure whether two datasets are similar or not. There are so many ways to calculate these values based on Data Type. Let's see into these methods -

### 1. For Binary Attribute:

Binary attributes are those which is having only two states 0 or 1, where 0 means attribute is absent and 1 means it is present. For calculating similarity/dissimilarity between binary attributes we use contingency table -

q - if i and j both are equal to 1
r - if i is 1 and j is 0
s - if i is 0 and j is 1
t - if i and j both are equal to 0
p - total ( q+r+s+t)

#### a. Symmetric Binary Dissimilarity -

For symmetric binary attribute, each state is equally valuable. If i and j are symmetric binary attribute then dissimilarity is calculates as -

  d(i, j) = \frac{r + s}{q + r + s + t}

#### b. Asymmetric Binary Dissimilarity -

For asymmetric binary attribute, two states are not equally important. Any one state overshadow the other, such binary attribute are often called "monary" (having one state). For these kind of attribute, dissimilarity is calculates as -

d(i, j) = \frac{r + s}{q + r + s}

likewise, we can calculate the similarity (asymmetric binary similarity)

 sim(i, j) = 1 - d(i, j)

which leave us with

 JC = sim(i, j) = \frac{q}{q + r + s}

The coefficient sim(i, j) is also known as Jaccard coefficient.

## Disclaimer

The postings on this site are my own and don't necessarily represent IBM's or other companies positions, strategies or opinions. All content provided on this blog is for informational purposes and knowledge sharing only.
The owner of this blog makes no representations as to the accuracy or completeness of any information on this site or found by following any link on this site. The owner will not be liable for any errors or omissions in this information nor for the availability of this information. The owner will not be liable for any losses, injuries, or damages from the display or use of his information.