Hypercluster: a flexible tool for parallelized unsupervised clustering optimization
L Blumenberg, KV Ruggles - BMC bioinformatics, 2020 - Springer
Background Unsupervised clustering is a common and exceptionally useful tool for large
biological datasets. However, clustering requires upfront algorithm and hyperparameter …
biological datasets. However, clustering requires upfront algorithm and hyperparameter …
Clustering and indexing of multiple documents using feature extraction through apache hadoop on big data
Bigdata is a challenging field in data processing since the information is retrieved from
various search engines through internet. A number of large organizations, that use …
various search engines through internet. A number of large organizations, that use …
A survey on surrogate approaches to non-negative matrix factorization
Motivated by applications in hyperspectral imaging, we investigate methods for
approximating a high-dimensional non-negative matrix Y by a product of two lower …
approximating a high-dimensional non-negative matrix Y by a product of two lower …
NMF based dimension reduction methods for Turkish text clustering
In this work, we analyze the effects of NMF based dimension reduction methods on
clustering of Turkish documents by using k-means clustering algorithm. All experiments are …
clustering of Turkish documents by using k-means clustering algorithm. All experiments are …
Multi-view Fuzzy Clustering Algorithm Based on Non-Negative Matrix Factorization and Partition Adaptive Fusion
X Tao, L Yu, X Wang - Proceedings of the 2019 2nd International …, 2019 - dl.acm.org
Nonnegative matrix decomposition (NMF), as a new method of matrix decomposition and
feature extraction, is an effective method of clustering by linearly separating data in high …
feature extraction, is an effective method of clustering by linearly separating data in high …
[引用][C] An analysis on effective and accurate data clustering based on Non-negative Matrix Factorization
A Vidhya, R Gunavathi