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DEGREE PROJECT IN MATHEMATICS, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2023Clustering and classification of prepaid mortgages Jakob Atli ThorsteinssonKTH ROYAL INSTITUTE OF TECHNOLOGY ELECTRICAL

How to fill out clustering and

How to fill out clustering and

1. Identify the dataset you want to cluster.
2. Determine the features or variables that are relevant for clustering.
3. Choose a clustering algorithm (e.g., K-means, hierarchical clustering, DBSCAN).
4. Preprocess the data (scale, normalize, handle missing values) if needed.
5. Split the dataset into training and testing sets, if applicable.
6. Run the chosen clustering algorithm on the data.
7. Determine the optimal number of clusters (for algorithms that require it) using methods like the elbow method or silhouette score.
8. Analyze the results by visualizing the clusters and interpreting the characteristics of each cluster.
9. Validate the clustering results using appropriate metrics (e.g., cohesion, separation).
10. Document your findings and any insights gained from the clustering.

Who needs clustering and?

1. Businesses looking to segment customers for targeted marketing strategies.
2. Researchers needing to analyze patterns in large datasets.
3. Healthcare providers wanting to group patients based on treatment response.
4. E-commerce companies aiming to recommend products by clustering buyer behavior.
5. Data analysts working on anomaly detection.
6. Social scientists studying group behaviors or patterns.

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What is clustering?

Clustering is a data analysis technique that groups similar data points together based on specific criteria, allowing for patterns and structures to be identified within the dataset.

Who is required to file clustering?

Individuals or organizations involved in data analysis, market research, or any processes requiring grouping of data for insights may be required to file clustering.

How to fill out clustering?

To fill out clustering, start by identifying the dataset, selecting appropriate clustering algorithms, defining parameters, and analyzing the results to interpret the grouped data.

What is the purpose of clustering?

The purpose of clustering is to identify patterns, relationships, or structures in data that can help in decision-making, predictive analysis, and enhancing understanding of complex datasets.

What information must be reported on clustering?

Information that must be reported on clustering includes the dataset used, the clustering method applied, parameters set, results obtained, and interpretations of the clustered data.