A Novel Agglomerative Hierarchical Approach for Clustering in Medical Databases

Thakran, Yogita; Toshniwal, Durga

doi:10.1007/978-3-319-01778-5_25

Cited by 2 publications

(1 citation statement)

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“…Research in text clustering has gained considerable attention in the past few years. Thakran et al [44] proposed a novel hierarchical agglomerative clustering algorithm, which uses a Cluster spread as the linkage metric for agglomeration and clustering threshold. Their method however processes datasets without any special representation or dimensionality reduction, which can be troublesome in text clustering, as the dimensionality of the vectorized documents in text mining is considerably high.…”

Section: Related Workmentioning

confidence: 99%

Text Mining using Nonnegative Matrix Factorization and Latent Semantic Analysis

Hassani¹,

Iranmanesh²,

Mansouri³

2019

Preprint

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Text clustering is arguably one of the most important topics in modern data mining. Nevertheless, text data require tokenization which usually yields a very large and highly sparse term-document matrix, which is usually difficult to process using conventional machine learning algorithms. Methods such as Latent Semantic Analysis have helped mitigate this issue, but are nevertheless not completely stable in practice. As a result, we propose a new feature agglomeration method based on Nonnegative Matrix Factorization, which is employed to separate the terms into groups, and then each groups term vectors are agglomerated into a new feature vector. Together, these feature vectors create a new feature space much more suitable for clustering. In addition, we propose a new deterministic initialization for spherical K-Means, which proves very useful for this specific type of data. In order to evaluate the proposed method, we compare it to some of the latest research done in this field, as well as some of the most practiced methods.In our experiments, we conclude that the proposed method either significantly improves clustering performance, or maintains the performance of other methods, while improving stability in results.

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Section: Related Workmentioning

confidence: 99%