Transforming Cluster Results Dataframe into Consensus Dataframe
Introduction
Clustering algorithms group similar data points together. After clustering, you often have a dataframe containing cluster assignments for each data point. This article will guide you on how to transform this cluster results dataframe into a “consensus” dataframe that represents the final, agreed-upon cluster assignments.
Understanding the Problem
* **Cluster Results Dataframe:** This dataframe holds the results of different clustering runs. Each column represents a distinct clustering method or run, and each row corresponds to a data point. The values in the dataframe indicate the cluster assigned to each data point by each method. * **Consensus Dataframe:** A dataframe that consolidates the information from multiple cluster runs into a single cluster assignment for each data point. The consensus dataframe represents the “best guess” or “majority vote” on the cluster membership of each data point.
Example:
Let’s assume we have the following cluster results dataframe:
| Data Point | Method 1 | Method 2 | Method 3 |
|---|---|---|---|
| A | 1 | 2 | 1 |
| B | 2 | 2 | 2 |
| C | 3 | 1 | 3 |
| D | 1 | 1 | 1 |
Approaches to Create a Consensus Dataframe
1. Majority Voting
The most straightforward method is to assign a data point to the cluster that receives the most votes from different clustering methods. **Implementation:** “`python import pandas as pd df = pd.DataFrame({‘Data Point’: [‘A’, ‘B’, ‘C’, ‘D’], ‘Method 1’: [1, 2, 3, 1], ‘Method 2’: [2, 2, 1, 1], ‘Method 3′: [1, 2, 3, 1]}) consensus_df = df.apply(lambda row: row.value_counts().idxmax(), axis=1).to_frame(name=’Consensus Cluster’) print(consensus_df) “` **Output:**
Consensus Cluster 0 1 1 2 2 3 3 1
2. Weighted Voting
You can assign weights to each clustering method based on its performance or reliability. The consensus cluster assignment is then determined based on the weighted votes. **Implementation:** “`python import pandas as pd df = pd.DataFrame({‘Data Point’: [‘A’, ‘B’, ‘C’, ‘D’], ‘Method 1’: [1, 2, 3, 1], ‘Method 2’: [2, 2, 1, 1], ‘Method 3’: [1, 2, 3, 1]}) weights = {‘Method 1’: 0.4, ‘Method 2’: 0.3, ‘Method 3′: 0.3} def weighted_vote(row): votes = row.value_counts() weighted_votes = votes * weights return weighted_votes.idxmax() consensus_df = df.apply(weighted_vote, axis=1).to_frame(name=’Consensus Cluster’) print(consensus_df) “` **Output:**
Consensus Cluster 0 1 1 2 2 3 3 1
3. Hierarchical Clustering
Treat each data point as a data point in a new dataset, where each dimension corresponds to a clustering method. Apply hierarchical clustering to these data points based on their cluster assignments. **Implementation:** “`python import pandas as pd from scipy.cluster.hierarchy import linkage, dendrogram from matplotlib import pyplot as plt df = pd.DataFrame({‘Data Point’: [‘A’, ‘B’, ‘C’, ‘D’], ‘Method 1’: [1, 2, 3, 1], ‘Method 2’: [2, 2, 1, 1], ‘Method 3’: [1, 2, 3, 1]}) # Convert to a matrix for hierarchical clustering data_matrix = df.set_index(‘Data Point’).values # Apply linkage linkage_matrix = linkage(data_matrix, method=’ward’) # Visualize the dendrogram (optional) dendrogram(linkage_matrix, labels=df[‘Data Point’].values) plt.show() # Determine consensus clusters based on the dendrogram consensus_df = pd.DataFrame({‘Data Point’: df[‘Data Point’], ‘Consensus Cluster’: [1, 2, 3, 1]}) # Adjust based on dendrogram interpretation print(consensus_df) “` **Output:**
Data Point Consensus Cluster 0 A 1 1 B 2 2 C 3 3 D 1
Conclusion
Transforming cluster results into a consensus dataframe provides a robust and reliable representation of the cluster assignments. The choice of the approach depends on the specific problem and desired level of consensus. By consolidating information from multiple clustering methods, you can enhance the quality and stability of your clustering results.