Clustering is a popular machine-learning technique that organizes a set of data into groups or clusters having similar characteristics.
In Python, there are several libraries like KMeans, DBSCAN, and AgglomerativeClustering in the Scikit-learn library that can perform clustering.
The result of these methods can be quite challenging to interpret, especially for beginners. Therefore, this tutorial will guide you on how to interpret clustering results in Python in a very practical way.
Step 1: Create a file
Create the “sample_data.csv” file with this content:
Column1,Column2 23,45 34,67 12,89 56,32 78,45 90,23 45,67 23,78 65,12 87,34
Step 2: Import Necessary Libraries
The first step is to import the necessary libraries. We will need pandas for data manipulation, matplotlib and Seaborn for data visualization, and sklearn for clustering.
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import pandas as pd<br>import matplotlib.pyplot as plt<br>import seaborn as sns<br>from sklearn.cluster import KMeans |
Step 3: Load the Dataset
The next step is to load the dataset that you want to perform clustering on. In this example, we will use a fictional dataset named “sample_data.csv”. The dataset can be loaded using pandas’ read_csv function.
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df = pd.read_csv('sample_data.csv') |
Step 4: Perform Clustering
After loading the data, you can now perform clustering. In this example, we will perform KMeans clustering.
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kmeans = KMeans(n_clusters=3) kmeans.fit(df) |
Step 5: Display the Clustering Result
Now, let’s see how the clusters have been formed. For that, we add a new column to the dataframe named “Cluster” which would contain the cluster each record belongs to.
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df['Cluster'] = kmeans.labels_ |
Step 6: Interpret the Clustering Result
Plotting the clustering result is a great way of interpreting the result. A scatter plot can be used where points of different clusters are marked with different colors. For example:
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plt.scatter(df['Column1'], df['Column2'], c=df['Cluster']) plt.show() |
Full Code
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import pandas as pd import matplotlib.pyplot as plt from sklearn.cluster import KMeans df = pd.read_csv('sample_data.csv') kmeans = KMeans(n_clusters=3) kmeans.fit(df) df['Cluster'] = kmeans.labels_ plt.scatter(df['Column1'], df['Column2'], c=df['Cluster']) plt.show() |
Conclusion
This tutorial demonstrated how you can interpret clustering results in Python. Interpreting these clustering results is essential as it allows you to understand the structure of your data and the relationships between different data points.
Keep practicing with various datasets and clustering methods to improve your interpretation skills.