Clustering is essential for identifying patterns in data by grouping similar points. However, many
advanced algorithms face challenges when dealing with clusters of varying shapes and sizes. In this
paper, we propose a KNN-FN hybrid algorithm combines the strengths of K-Nearest Neighbors (KNN)
and the Fast Newman (FN) community detection algorithm to enhance clustering performance. KNN
is used to construct a graph that captures local neighborhood structures by connecting each data point
to its nearest neighbors, while the FN algorithm applies modularity maximization to detect well- defined clusters within the graph. This hybrid approach improves clustering, particularly in complex datasets with irregular shapes and
varying densities, The KNN-FN hybrid algorithm efficiently detects clusters in large-scale data, making it suitable for real-world applications.
Citation
Nassira LOGRADA ,
Makhlouf Benazi ,
,(2025-11-25), A Graph-Based Hybrid Clustering Approach for Detecting Complex Structures,The 2nd International Workshop on Machine Learning and Deep Learning (WMLDL25),msila
