Binning in Data Mining

Binning in Data Mining

Binning in Data Mining is also known as discretization or bucketing. It is a data preprocessing technique used in data mining.The quantity of bins plays an important role in Binning in Data Mining. It is used to categorize data into discrete bins based on the range or values of data points. Binning methods use neighboring data points for smoothing them. This is called local smoothing. Binning is dividing a continuous variable into discrete categories by creating bins. There are different methods used to divide data into bins:

Equal Width Binning

Equal Width Binning is a data mining technique that divides a continuous variable into bins of equal widths or intervals. This approach aims to group data points into bins with the same range or width.

To understand this better, let's take an example of equal-width binning.

Dataset points: 10, 15, 18, 20, 31, 34, 41, 46, 51, 53, 54 
 

Step 1: Sort the dataset in ascending order: 10, 15, 18, 20, 31, 34, 41, 46, 51, 53, 54.
 

Step 2: Determine the width of each bin using the formula: w = (max-min) / N, where w is the width, max is the maximum value, min is the minimum value, and N is the number of bins.

In our example, the minimum value is 10, the maximum value is 54, and we choose four bins. So, the width (w) is calculated as (54 - 10) / 4 = 11. Now we have to add value to each bin which would be as follows:

BIN 1 : [lower bound , upper bound] = [(min) , (min + w -1)]  = [10, 20]
BIN 2 : [lower bound , upper bound] = [(min + w) , (min + 2w -1)] = [21, 31] 
BIN 3 : [lower bound , upper bound] = [(min + 2w) , (min + 3w -1)]  = [32, 42]
BIN 4 : [lower bound , upper bound] = [(min + 3w) , (max)] = [43, 54]

So each bin would contain values in between their lower bound and upper bound. So the final bin for this dataset would have values like:

BIN 1 : [10, 15, 18, 20]
BIN 2 : [31]
BIN 3 : [34, 41]
BIN 4 : [46, 51, 53, 54]

Equal Width Binning is a straightforward approach in which continuous data is divided into discrete bins. It's important to consider the data's nature and the analysis's specific requirements when choosing the number of bins and applying equal-width binning.

Equal Frequency Binning

Equal Frequency Binning can be referred as Equal Depth Binning. It is a data binning technique that divides the values into bins having equal number of observations or frequency.This method's major goal is to ensure every bin has a comparable amount of data points. This helps us to present the information in a balanced manner.

Consider the following example for equal frequency binning.

Dataset: 10,15, 18, 20, 31, 34, 41, 46, 51, 53, 54, 60

First, we have to sort the data. If we have unsorted data, we need to convert it into sorted data and then apply the second step. In the second step, we have to find the frequency. To calculate the frequency, we can use the formula a total number of data points/number of bins.

In this case, the total number of data points is 12, and the number of bins required is 3. Therefore, the frequency comes out to be 4. Now let's add values into the bins. 

BIN 1: 10, 15, 18, 20
BIN 2: 31, 34, 41, 46
BIN 3: 51, 53, 54, 60

This method is beneficial in the case of skewed or non-uniformed data. Equal Frequency Binning helps in representing a balanced distribution of the data points.

Data Smoothing

Data Smoothing is a preprocessing method that is used to reduce the noise from the dataset. They are applied after the binning process to simplify the data representation within the bin further. After partitioning, the developer can perform smoothing techniques like:

• Smooth by bin mean - replace the noisy values in the bin with the bin mean value.
   
• Smooth by bin median - replace the noisy values in the bin with the bin median value.
   
• Smooth by bin boundaries - replace the noisy value in the bin with the bin boundaries value.
   

Outliers can also be solved with the binning technique. To detect outliers, we can use graphs or boxplots. We can also use clustering methods to identify outliers and then use smoothing techniques.

Look at the example below to understand how smoothing works in a dataset.

There is sorted data of the marks in the class of the students. We aim to categorize the students into groups and pay more attention to those who have received low marks.

Sorted data : 4, 7, 13, 16, 20, 24, 27, 29, 31, 33, 38, 42.

Partition into (Equal-Frequency) Bin

Let's Consider following example, to better understand Equal Frequency Bin.

BIN 1: 4, 7, 13, 16
BIN 2: 20, 24, 27, 29
BIN 3: 31, 33, 38, 42

In this approach, the dataset is sorted and is divided into equal-frequency bins with a depth of 4. By using equal-frequency binning in this example, we have created three bins with an equal number of data points in each bin. This partitioning technique helps us in studying detailed analysis of the dataset.

Smoothing by Bin Mean Method

Let's Consider following example, to better understand Bin Mean Method.

BIN 1 : 10, 10, 10, 10 (mean: 10)
BIN 2 : 25, 25, 25, 25 (mean: 25)
BIN 3 : 36, 36, 36, 36 (mean: 36)

In this method, after smoothing, all the values are replaced with the mean value of the bin. It focuses on the average or central tendency within each bin. The smoothing by mean bin method can help in reducing the impact of the outliers or extreme values within each bin. 

Smoothing by Bin Boundaries Method

Let's Consider following example, to better understand Bin Mean Method.

BIN 1: 4, 4, 16, 16  (min: 4 and max: 16)
BIN 2: 20, 20, 29, 29 (min: 20 and max: 29)
BIN 3: 31, 31, 38, 42 (min: 31 and max: 42)

In this method, after smoothing, each value is replaced with the minimum and maximum values of the bin. In this case, the values within the BIN 1 , the value 7 is near to value 4, so 7 will be replaced with the value 4. The value 13 is nearer to its maximum value so it will be replaced with 16.So the final value in the BIN 1 is [4, 4, 16, 16]. The same step have been repeated for all other bins. By using the smoothing by bin boundaries method, it helps in reducing the outliers value's impact and provides a clearer dataset for evaluation.

Advantages of Binning in Data Mining

There are many advantages of Binning in Data Mining such as:

Noise reduction: Rephasing helps reduce noise or non-linearities in the dataset.
 

Outlier identification: Rephasing assists in identifying outliers and missing values in numerical variables.
 

Hidden pattern discovery: Rephasing helps uncover hidden patterns within the dataset.
 

Ease of implementation and interpretation: Rephasing techniques are easy to implement and understand.
 

Overfitting prevention: Rephasing helps reduce the likelihood of overfitting in models.

Use Case of Binning in Data Mining

Here are some of the use cases where Binning can be used:

Customer Segmentation

Binning can be used in customer segmentation. A business can optimize its marketing strategies using the Binning method in customer segmentation. 

We can use binning on continuous variables such as purchase amount, transaction, order placed, or category chosen. By separately creating these bins, it will help the business to make a target audience for a particular marketing campaign.

Check this out, Agents in Artificial Intelligence

Frequently Asked Questions

What is the binning process?

The binning process groups continuous data into discrete intervals (bins) to reduce noise, smooth data, and improve analysis in machine learning and statistics.

What is the meaning of binning of data?

Binning of data means dividing numerical values into fixed intervals (bins) to simplify patterns, reduce variability, and improve data preprocessing in analytics.

What is the difference between clustering and binning?

Clustering groups similar data points dynamically, while binning divides data into predefined fixed intervals without considering similarity among values.

Conclusion

Binning is a valuable technique in Data Mining. It allows us to transform continuous variables into discrete categories or bins. It simplifies data analysis, improves interpretability, and enables us to recognize patterns and trends. If you are working with a large dataset, data visualization is challenging, or when an algorithm and model require categorical data. In all these cases, Binning is helpful. In this article, we discuss different techniques involved in Binning. Standard techniques are Equal Width Binning and Equal Frequency Binning. We even explore other Data Smoothing techniques which are associated with Binning. Data Smoothing techniques are the bin mean method and bin boundary method. We even discuss some use cases of Binning in Data Mining. Overall, Binning is a versatile tool in data mining.

To learn more about this topic, check out the link below:

• Data Mining Process.
   
• Data Mining Architecture.
   
• Applications of Data Mining.