<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, a modified bat algorithm with fuzzy inference mamdani-type system is applied to the problem of document clustering in a semantic features space induced by SVD decomposition. The algorithm learns the optimal clustering of the documents as well as the optimal number of clusters in a concept space; thus, making it suitable for a large and spare dataset which occur in information retrieval system. a centroid-based solution in multidimensional space is evaluated with a silhouette index. A TF-IDF method is used to represent documents in vector space. The presented algorithm is tested on the 20 newsgroup dataset.</p></abstract>

In this paper, a modified bat algorithm with fuzzy inference mamdani-type system is applied to the problem of document clustering in a semantic features space induced by SVD decomposition. The algorithm learns the optimal clustering of the documents as well as the optimal number of clusters in a concept space; thus, making it suitable for a large and spare dataset which occur in information retrieval system. a centroid-based solution in multidimensional space is evaluated with a silhouette index. A TF-IDF method is used to represent documents in vector space. The presented algorithm is tested on the 20 newsgroup dataset.

Unsupervised learning in latent space with a fuzzy logic guided modified BA

Department of Computer Science, Faculty of Electrical and Computer Engineering

Technical Transactions

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 Public License.

{"article-title":"Unsupervised learning in latent space with a fuzzy logic guided modified BA"}

In this paper, a modified bat algorithm with fuzzy inference mamdani-type system is applied to the problem of document clustering in a semantic features space...

Unsupervised learning in latent space with a fuzzy logic guided modified BA

Online veröffentlicht: 21. Mai 2020

Seitenbereich: 141 - 153

Eingereicht: 17. Juli 2018

DOI: https://doi.org/10.4467/2353737XCT.18.121.8896

Schlüsselwörter
document clustering, unsupervised machine learning, BA, fuzzy logic

© 2019 Kazimierz Kiełkowicz, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 Public License.

Unsupervised learning in latent space with a fuzzy logic guided modified BA

Online veröffentlicht: 21. Mai 2020

Seitenbereich: 141 - 153

Eingereicht: 17. Juli 2018

DOI: https://doi.org/10.4467/2353737XCT.18.121.8896

Schlüsselwörterdocument clustering, unsupervised machine learning, BA, fuzzy logic

© 2019 Kazimierz Kiełkowicz, published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 Public License.

Schlüsselwörter
document clustering, unsupervised machine learning, BA, fuzzy logic