Computer Science
5 years ago

New MST Clustering Algorithm Outperforms Traditional Methods for Data Analysis

International Journal of Computer Applications
Comparison of Parameter Free MST Clustering Algorithm with Hierarchical Agglomerative Clustering Algorithms
Raju Bhvs Ramakrishnam, Kumari.V Valli

Paper Summary

The article compares different clustering algorithms to group similar data together. The researchers looked at hierarchical agglomerative clustering algorithms and a Parameter Free Minimum Spanning Tree (MST) clustering algorithm. They found that the MST algorithm produced better clustering results than the hierarchical agglomerative algorithms. This suggests that the MST algorithm is a good choice for clustering data effectively.

Related papers

Theme background image
Computer Science
7 years ago

Revolutionizing Data Analysis: K-means Clustering Unlocks Big Data Insights

Data Clustering
2 views
Theme background image
Computer Science
8 years ago

Unleashing the Power of Data Mining: Discovering the Best Clustering Algorithms

Data Mining Techniques
Data Clustering
Trajectory Analysis
2 views
Theme background image
Computer Science
8 years ago

New hybrid clustering algorithm improves accuracy and efficiency in data analysis.

Data Clustering
Dimensionality Reduction
Concept Drift
1 view