Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Clustering techniques play a crucial role in the field of data mining, offering significant benefits for knowledge engineering. This paper explores the application of various clustering algorithms, including k-means, hierarchical clustering, and DBSCAN, to improve knowledge organization and retrieval. We present a detailed analysis of the strengths and weaknesses of these algorithms in different scenarios, emphasizing their practical implementation in real-world knowledge engineering systems. Through experimental studies and practical examples, we showcase how clustering techniques can facilitate the efficient categorization and management of large datasets, ultimately enhancing the overall knowledge engineering process.

Keywords: Clustering, Knowledge Engineering, K-means, Hierarchical Clustering, DBSCAN