Showing posts with label Plotting. Show all posts
Showing posts with label Plotting. Show all posts

## Sunday, 4 February 2018

### Multiple Plots into One Figure in Python MatplotLib

Lets learn, How to plot multiple charts into one Figure. Embedding a jupyter notebook here with the examples.

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## Tuesday, 9 May 2017

### Measuring Data Similarity or Dissimilarity #1

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.