A Novel Cluster Prediction Approach Based on Locality-Sensitive Hashing for Fuzzy Clustering of Categorical Data
This paper addresses the problem of fuzzy clustering for categorical data. During the last two decades, many attempts have been made to extend the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-means algorithm, making it applicable to c...
Main Authors: | Toan Nguyen Mau, Yasushi Inoguchi, Van-Nam Huynh |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2022-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9743464/ |
Similar Items
-
Clustering feature vectors with mixed numerical and categorical attributes
by: R.K. Brouwer
Published: (2008-12-01) -
Automatic Fuzzy Clustering Using Non-Dominated Sorting Particle Swarm Optimization Algorithm for Categorical Data
by: Thi Phuong Quyen Nguyen, et al.
Published: (2019-01-01) -
Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices
by: Toan Nguyen Mau, et al.
Published: (2020-04-01) -
Learning-Based Dissimilarity for Clustering Categorical Data
by: Edgar Jacob Rivera Rios, et al.
Published: (2021-04-01) -
An Optimal and Stable Algorithm for Clustering Numerical Data
by: Ali Seman, et al.
Published: (2021-06-01)