Abstract
The clustering of documents based on their similarity is prolific for an application that wants to extract similar documents and disparate non-similar documents to reduce ambiguity in finding relevant documents. Therefore, we require a robust clustering algorithm that can cluster document efficiently and effectively. In this paper, the performance of two clustering algorithms K-Means and DBSCAN with optimal parameters are compared on various textual datasets with distance measures—cosine and hybrid similarity. The challenge of finding the optimal value of epsilon in DBSCAN and value of K in K-Means is fulfilled by the DMDBSCAN and within-cluster sum of square algorithms, respectively. Hybrid similarity has an impact of single words and phrases, so the shared phrases across the corpus are drawn and the phrase similarity is computed. Then, hybrid similarity is formed using cosine and Phrase. To catch the shared phrases, the document representation model—Document Index Graph—is implemented in the Neo4j graph database. We utilized silhouette score to evaluate the performance of clustering algorithms. Experimental results reflect that DBSCAN performs better than K-Means on both the similarity measures.
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Kathiria, P., Pandya, V., Arolkar, H., Patel, U. (2023). Performance Analysis of Document Similarity-Based DBSCAN and K-Means Clustering on Text Datasets. In: Singh, Y., Singh, P.K., Kolekar, M.H., Kar, A.K., Gonçalves, P.J.S. (eds) Proceedings of International Conference on Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 1001. Springer, Singapore. https://doi.org/10.1007/978-981-19-9876-8_5
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