Orthogonal nonnegative matrix tri-factorization based on Tweedie distributions

Abe, Hiroyasu; Yadohisa, Hiroshi

doi:10.1007/s11634-018-0348-8

Cited by 9 publications

(5 citation statements)

References 37 publications

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“…(7); Update F h according to Eq. (8); 4: Return F h , S h , and G h NMFRU with the well-known clustering methods, such as Kmeans, NMF, and the commonly used variants, including Semi-NMF (SNMF) [43] and Orthogonal NMTF (ONMTF) [44] .…”

Section: Resultsmentioning

confidence: 99%

Coronavirus pandemic analysis through tripartite graph clustering in online social networks

Liao

Zheng

Cao

2021

Big Data Min. Anal.

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The COVID-19 pandemic has hit the world hard. The reaction to the pandemic related issues has been pouring into social platforms, such as Twitter. Many public officials and governments use Twitter to make policy announcements. People keep close track of the related information and express their concerns about the policies on Twitter. It is beneficial yet challenging to derive important information or knowledge out of such Twitter data. In this paper, we propose a Tripartite Graph Clustering for Pandemic Data Analysis (TGC-PDA) framework that builds on the proposed models and analysis: (1) tripartite graph representation, (2) non-negative matrix factorization with regularization, and (3) sentiment analysis. We collect the tweets containing a set of keywords related to coronavirus pandemic as the ground truth data. Our framework can detect the communities of Twitter users and analyze the topics that are discussed in the communities. The extensive experiments show that our TGC-PDA framework can effectively and efficiently identify the topics and correlations within the Twitter data for monitoring and understanding public opinions, which would provide policy makers useful information and statistics for decision making.

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Section: Resultsmentioning

confidence: 99%

Coronavirus pandemic analysis through tripartite graph clustering in online social networks

Liao

Zheng

Cao

2021

Big Data Min. Anal.

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“…The second strategy implements an analysis that leverages the 𝑆 matrix as a means to establish relationships between clusters of data and clusters of attributes. Studies investigating this matrix [Abe andYadohisa 2019, Freitas Junior et al 2020] hypothesize that the values within its cells reflect the degree to which a group of words can effectively describe a group of documents, and vice versa. This strategy presents similarities to the first one but incorporates information specifically associated with coclusters.…”

Section: Related Workmentioning

confidence: 99%

A strategy for interpreting and visualizing the results of matrix-trifactorization-based coclustering algorithms

Castro,

Peres,

Freitas Junior

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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Information yielded by unsupervised learning is often hard to interpret due to the lack of defined labels. To overcome this, we propose and illustrate a strategy for interpreting and visualizing the results of coclustering algorithms based on trifactorization. Our method consists of three steps: (1) vector space visualization; (2) cluster characterization by top documents/words; and (3) cocluster characterization by comparing top words between different clusters. The latter allows exploring the resulting clusters in a way which considers the relationship between attribute cluster and data cluster for every data cluster, instead of just the data cluster with the highest association with this attribute cluster. We illustrate the use of our method for the Non-negative Block Value Decomposition on a dataset of scientific abstracts.

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“…As an important technology of data mining and information granular construction (Xu et al, 2022), the traditional clustering algorithms analyze only the properties of the data samples (the number and types of the attributes or variables), but do not focus on the components of data such as Data-table, Data-column, Data-relation etc. This is a major issue affecting the clustering performance (Abe and Yadohisa, 2019), especially when dealing with high-dimensional genes expression data, which motivates the development of the bi-clustering algorithm. Bi-clustering is not only able to reveal the global structure (as the traditional methods do) in data, but also able to discover the local information (it can discover clusters in the feature space and the data space simultaneously).…”

Section: Introductionmentioning

confidence: 99%

An enhanced adaptive Bi-clustering algorithm through building a shielding complex sub-matrix

Tang

Yin

et al. 2022

Front. Genet.

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Bi-clustering refers to the task of finding sub-matrices (indexed by a group of columns and a group of rows) within a matrix of data such that the elements of each sub-matrix (data and features) are related in a particular way, for instance, that they are similar with respect to some metric. In this paper, after analyzing the well-known Cheng and Church bi-clustering algorithm which has been proved to be an effective tool for mining co-expressed genes. However, Cheng and Church bi-clustering algorithm and summarizing its limitations (such as interference of random numbers in the greedy strategy; ignoring overlapping bi-clusters), we propose a novel enhancement of the adaptive bi-clustering algorithm, where a shielding complex sub-matrix is constructed to shield the bi-clusters that have been obtained and to discover the overlapping bi-clusters. In the shielding complex sub-matrix, the imaginary and the real parts are used to shield and extend the new bi-clusters, respectively, and to form a series of optimal bi-clusters. To assure that the obtained bi-clusters have no effect on the bi-clusters already produced, a unit impulse signal is introduced to adaptively detect and shield the constructed bi-clusters. Meanwhile, to effectively shield the null data (zero-size data), another unit impulse signal is set for adaptive detecting and shielding. In addition, we add a shielding factor to adjust the mean squared residue score of the rows (or columns), which contains the shielded data of the sub-matrix, to decide whether to retain them or not. We offer a thorough analysis of the developed scheme. The experimental results are in agreement with the theoretical analysis. The results obtained on a publicly available real microarray dataset show the enhancement of the bi-clusters performance thanks to the proposed method.

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Orthogonal nonnegative matrix tri-factorization based on Tweedie distributions

Cited by 9 publications

References 37 publications

Coronavirus pandemic analysis through tripartite graph clustering in online social networks

Coronavirus pandemic analysis through tripartite graph clustering in online social networks

A strategy for interpreting and visualizing the results of matrix-trifactorization-based coclustering algorithms

An enhanced adaptive Bi-clustering algorithm through building a shielding complex sub-matrix

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