scholarly journals A Mutual Information based Face Clustering Algorithm for Movies

2006 ◽  
Author(s):  
N. Vretos ◽  
V. Solachidis ◽  
I. Pitas
Keyword(s):  
Mutual Information ◽  
Clustering Algorithm ◽  
Face Clustering

scholarly journals Simple primitive recognition via hierarchical face clustering

2020 ◽  
Vol 6 (4) ◽  
pp. 431-443
Author(s):  
Xiaolong Yang ◽  
Xiaohong Jia
Keyword(s):  
Hierarchical Clustering ◽  
Test Data ◽  
Efficient Algorithm ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Experimental Results ◽  
Face Clustering ◽  
Wide Range ◽  
Hierarchical Clustering Algorithm ◽  
Bottom To Top

AbstractWe present a simple yet efficient algorithm for recognizing simple quadric primitives (plane, sphere, cylinder, cone) from triangular meshes. Our approach is an improved version of a previous hierarchical clustering algorithm, which performs pairwise clustering of triangle patches from bottom to top. The key contributions of our approach include a strategy for priority and fidelity consideration of the detected primitives, and a scheme for boundary smoothness between adjacent clusters. Experimental results demonstrate that the proposed method produces qualitatively and quantitatively better results than representative state-of-the-art methods on a wide range of test data.

Modified Global K-Means Clustering Algorithm Using Mutual Information

2013 ◽  
Vol 19 (1) ◽  
pp. 212-215
Author(s):  
Chang-Woo Seo ◽  
Bo Kyung Cha ◽  
Ryun Kyung Kim ◽  
Sungchae Jeon ◽  
Young Huh ◽  
...  
Keyword(s):  
Mutual Information ◽  
Clustering Algorithm

A Semantic-Based Algorithm for Microblogs Clustering

2013 ◽  
Vol 278-280 ◽  
pp. 1174-1177 ◽  
Author(s):  
Jia Jia Miao ◽  
Guo You Chen ◽  
Le Wang ◽  
Xue Lin Fang
Keyword(s):  
Mutual Information ◽  
Clustering Algorithm ◽  
Frequent Itemsets ◽  
Experimental Results ◽  
High Dimensional ◽  
Large Capacity ◽  
Current Affairs ◽  
Context Knowledge ◽  
Share Information

Microblogging has become a major tool for people to not only share information, but also to talk about current affairs. Has become the most popular content in the analysis, interested companies and researchers. We focus on the micro-blog clustering high-dimensional, high sparse, and proposed a new algorithm based on k-means-k frequent itemsets. In addition, the development of a method to capture long-term mutual information context knowledge in microblogging and algorithms are also designed to measure the conversation Similar. In order to support the new micro-blog clustering algorithm. Experimental results show that the clustering algorithm has higher accuracy than the standard k-means and two points in k-means algorithm toward large-capacity and highly sparse microblogging also maintain good scalability.

scholarly journals SMILE: Mutual Information Learning for Integration of Single Cell Omics Data

2021 ◽  
Author(s):  
Yang Xu ◽  
Priyojit Das ◽  
Rachel Patton McCord
Keyword(s):  
Mutual Information ◽  
Single Cell ◽  
Clustering Algorithm ◽  
Cell Types ◽  
Cellular Systems ◽  
Omics Data ◽  
Learning Approaches ◽  
Rna Seq ◽  
Cell Transcriptome ◽  
Unique Cell

Deep learning approaches have empowered single-cell omics data analysis in many ways, generating new insights from complex cellular systems. As there is an increasing need for single cell omics data to be integrated across sources, types, and features of data, the challenges of integrating single-cell omics data are rising. Here, we present a deep clustering algorithm that learns discriminative representation for single-cell data via maximizing mutual information, SMILE (Single-cell Mutual Information Learning). Using a unique cell-pairing design, SMILE successfully integrates multi-source single-cell transcriptome data, removing batch effects and projecting similar cell types, even from different tissues, into the same representation space. SMILE can also integrate data from two or more modalities, such as joint profiling technologies using single-cell ATAC-seq, RNA-seq, DNA methylation, Hi-C, and ChIP data. SMILE works well even when feature types are unmatched, such as genes for RNA-seq and genome wide peaks for ATAC-seq.

Study on Mutual Information Based Clustering Algorithm

2007 ◽  
Vol 6 (2) ◽  
pp. 251-254 ◽  
Author(s):  
Hongfang Zhou ◽  
Boqin Feng ◽  
Lintao Lv ◽  
Hui Yue
Keyword(s):  
Mutual Information ◽  
Clustering Algorithm

GNN-DBSCAN: A new density-based algorithm using grid and the nearest neighbor

2021 ◽  
pp. 1-13
Author(s):  
Li Yihong ◽  
Wang Yunpeng ◽  
Li Tao ◽  
Lan Xiaolong ◽  
Song Han
Keyword(s):  
Mutual Information ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Adjusted Rand Index ◽  
K Nearest Neighbors ◽  
Normalized Mutual Information ◽  
Core Samples ◽  
Real World Datasets

DBSCAN (density-based spatial clustering of applications with noise) is one of the most widely used density-based clustering algorithms, which can find arbitrary shapes of clusters, determine the number of clusters, and identify noise samples automatically. However, the performance of DBSCAN is significantly limited as it is quite sensitive to the parameters of eps and MinPts. Eps represents the eps-neighborhood and MinPts stands for a minimum number of points. Additionally, a dataset with large variations in densities will probably trap the DBSCAN because its parameters are fixed. In order to overcome these limitations, we propose a new density-clustering algorithm called GNN-DBSCAN which uses an adaptive Grid to divide the dataset and defines local core samples by using the Nearest Neighbor. With the help of grid, the dataset space will be divided into a finite number of cells. After that, the nearest neighbor lying in every filled cell and adjacent filled cells are defined as the local core samples. Then, GNN-DBSCAN obtains global core samples by enhancing and screening local core samples. In this way, our algorithm can identify higher-quality core samples than DBSCAN. Lastly, give these global core samples and use dynamic radius based on k-nearest neighbors to cluster the datasets. Dynamic radius can overcome the problems of DBSCAN caused by its fixed parameter eps. Therefore, our method can perform better on dataset with large variations in densities. Experiments on synthetic and real-world datasets were conducted. The results indicate that the average Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Adjusted Mutual Information (AMI) and V-measure of our proposed algorithm outperform the existing algorithm DBSCAN, DPC, ADBSCAN, and HDBSCAN.

UAC: An Uncertainty-Aware Face Clustering Algorithm

2021 ◽  
Author(s):  
Biplob Debnath ◽  
Giuseppe Coviello ◽  
Yi Yang ◽  
Srimat Chakradhar
Keyword(s):  
Clustering Algorithm ◽  
Face Clustering
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