scholarly journals Converting Ensemble Clustering Problem to a Mathematical Optimization Problem and Providing an Approach to Solve Based on Optimization Toolbox

2018 ◽  
Author(s):  
Simintaj Salehpour ◽  
Hamid Parvin
Keyword(s):  
High Performance ◽  
Clustering Algorithms ◽  
Critical Role ◽  
Mathematical Optimization ◽  
Ensemble Clustering ◽  
Clustering Methods ◽  
Clustering Problem ◽  
Lack Of Information ◽  
Stable Clusters ◽  
Information Retrieval Methods

Nowadays, we live in a world in which people are facing with a lot of data that should be stored or displayed. One of the key methods to control and manage this data refers to grouping and classifying them in clusters. Today, clustering has a critical role in information retrieval methods for organizing large collections inside a few significant clusters. One of the main motivations for the use of clustering is to determine and reveal the hidden and inherent structure of a set of data. Ensemble clustering algorithms combine multiple clustering algorithms to finally reach an overall clustering system. Ensemble clustering methods by lack of information fusing utilize several primary partitions of data to find better ways. Since various clustering algorithms look at the different data points, they can produce various partitions from such data. It is possible to create a partition with high performance by combining the partitions obtained from different algorithms, even if the clusters to be very dense from each other. Most studies in this area have examined all the initial clusters. In this study, a new method is used in which the most sustainable clusters are utilized instead of all primary produced clusters. Consensus function based on co-association matrixes used to select more stable clusters. The most stable clusters selection method is done by cluster stability criterion based on F-measure. Optimization functions are used to optimize the obtained final clusters. The genetic algorithm is the optimizer used in this article to find the ultimate clusters participated in a consensus. Experimental results on several datasets show that the output of proposed method is various clusters with high stability.

Cluster Analysis Using Rough Clustering and k-Means Clustering

2011 ◽  
pp. 561-565
Author(s):  
Kevin E. Voges
Keyword(s):  
Cluster Analysis ◽  
Computational Intelligence ◽  
Rough Sets ◽  
Information Science ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Rough Sets Theory ◽  
Clustering Problem ◽  
Data Reduction Technique ◽  
Sets Theory

Cluster analysis is a fundamental data reduction technique used in the physical and social sciences. It is of potential interest to managers in Information Science, as it can be used to identify user needs though segmenting users such as Web site visitors. In addition, the theory of Rough sets is the subject of intense interest in computational intelligence research. The extension of this theory into rough clustering provides an important and potentially useful addition to the range of cluster analysis techniques available to the manager. Cluster analysis is defined as the grouping of “individuals or objects into clusters so that objects in the same cluster are more similar to one another than they are to objects in other clusters” (Hair, Black, Babin, Anderson, & Tatham, 2006). There are a number of comprehensive introductions to cluster analysis (Abonyi & Feil, 2007; Arabie, Hubert, & De Soete, 1994; Cramer, 2003; Everitt, Landau, & Leese, 2001; Gan, Ma, & Wu, 2007; Härdle & Hlávka, 2007). Techniques are often classified as hierarchical or nonhierarchical (Hair et al., 2006), and the most commonly used nonhierarchical technique is the k-means approach developed by MacQueen (1967). Recently, techniques based on developments in computational intelligence have also been used as clustering algorithms. For example, the theory of fuzzy sets developed by Zadeh (1965), which introduced the concept of partial set membership, has been applied to clustering (Abonyi & Feil, 2007; Dumitrescu, Lazzerini, & Jain, 2000). Another technique receiving considerable attention is the theory of rough sets (Pawlak, 1982), which has led to clustering algorithms referred to as rough clustering (do Prado, Engel, & Filho, 2002; Kumar, Krishna, Bapi, & De, 2007; Parmar, Wu, & Blackhurst, 2007; Voges, Pope, & Brown, 2002). This article provides brief introductions to k-means cluster analysis, rough sets theory, and rough clustering, and compares k-means clustering and rough clustering. It shows that rough clustering provides a more flexible solution to the clustering problem, and can be conceptualized as extracting concepts from the data, rather than strictly delineated subgroupings (Pawlak, 1991). Traditional clustering methods generate extensional descriptions of groups (i.e., which objects are members of each cluster), whereas clustering techniques based on rough sets theory generate intentional descriptions (i.e., what are the main characteristics of each cluster) (do Prado et al., 2002). These different goals suggest that both k-means clustering and rough clustering have their place in the data analyst’s and the information manager’s toolbox.

Ant Custering Algorithms

2010 ◽  
Vol 1 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Yu-Chiun Chiou ◽  
Shih-Ta Chou
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Small Scale ◽  
Solution Stability ◽  
Clustering Methods ◽  
Scale Problem ◽  
Clustering Problem ◽  
Genetic Clustering ◽  
Fully Connected ◽  
Pheromone Trail

This paper proposes three ant clustering algorithms (ACAs): ACA-1, ACA-2 and ACA-3. The core logic of the proposed ACAs is to modify the ant colony metaheuristic by reformulating the clustering problem into a network problem. For a clustering problem of N objects and K clusters, a fully connected network of N nodes is formed with link costs, representing the dissimilarity of any two nodes it connects. K ants are then to collect their own nodes according to the link costs and following the pheromone trail laid by previous ants. The proposed three ACAs have been validated on a small-scale problem solved by a total enumeration method. The solution effectiveness at different problem scales consistently shows that ACA-2 outperforms among these three ACAs. A further comparison of ACA-2 with other commonly used clustering methods, including agglomerative hierarchy clustering algorithm (AHCA), K-means algorithm (KMA) and genetic clustering algorithm (GCA), shows that ACA-2 significantly outperforms them in solution effectiveness for the most of cases and also performs considerably better in solution stability as the problem scales or the number of clusters gets larger.

scholarly journals Study on the Influence of Diversity and Quality in Entropy Based Collaborative Clustering

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 951
Author(s):  
Jérémie Sublime ◽  
Guénaël Cabanes ◽  
Basarab Matei
Keyword(s):  
Clustering Algorithms ◽  
Mathematical Optimization ◽  
Data Sets ◽  
Distributed Data ◽  
Clustering Methods ◽  
Local Structures ◽  
Collaborative Clustering ◽  
Privacy Constraints ◽  
The Stability ◽  
The One

The aim of collaborative clustering is to enhance the performances of clustering algorithms by enabling them to work together and exchange their information to tackle difficult data sets. The fundamental concept of collaboration is that clustering algorithms operate locally but collaborate by exchanging information about the local structures found by each algorithm. This kind of collaborative learning can be beneficial to a wide number of tasks including multi-view clustering, clustering of distributed data with privacy constraints, multi-expert clustering and multi-scale analysis. Within this context, the main difficulty of collaborative clustering is to determine how to weight the influence of the different clustering methods with the goal of maximizing the final results and minimizing the risk of negative collaborations—where the results are worse after collaboration than before. In this paper, we study how the quality and diversity of the different collaborators, but also the stability of the partitions can influence the final results. We propose both a theoretical analysis based on mathematical optimization, and a second study based on empirical results. Our findings show that on the one hand, in the absence of a clear criterion to optimize, a low diversity pool of solution with a high stability are the best option to ensure good performances. And on the other hand, if there is a known criterion to maximize, it is best to rely on a higher diversity pool of solution with a high quality on the said criterion. While our approach focuses on entropy based collaborative clustering, we believe that most of our results could be extended to other collaborative algorithms.

Ant Custering Algorithms

2012 ◽  
pp. 1-15
Author(s):  
Yu-Chiun Chiou ◽  
Shih-Ta Chou
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Small Scale ◽  
Solution Stability ◽  
Clustering Methods ◽  
Clustering Problem ◽  
The Core ◽  
Genetic Clustering ◽  
Fully Connected ◽  
Pheromone Trail

This paper proposes three ant clustering algorithms (ACAs): ACA-1, ACA-2 and ACA-3. The core logic of the proposed ACAs is to modify the ant colony metaheuristic by reformulating the clustering problem into a network problem. For a clustering problem of N objects and K clusters, a fully connected network of N nodes is formed with link costs, representing the dissimilarity of any two nodes it connects. K ants are then to collect their own nodes according to the link costs and following the pheromone trail laid by previous ants. The proposed three ACAs have been validated on a small-scale problem solved by a total enumeration method. The solution effectiveness at different problem scales consistently shows that ACA-2 outperforms among these three ACAs. A further comparison of ACA-2 with other commonly used clustering methods, including agglomerative hierarchy clustering algorithm (AHCA), K-means algorithm (KMA) and genetic clustering algorithm (GCA), shows that ACA-2 significantly outperforms them in solution effectiveness for the most of cases and also performs considerably better in solution stability as the problem scales or the number of clusters gets larger.

CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM

2007 ◽  
Vol 16 (06) ◽  
pp. 919-934
Author(s):  
YONGGUO LIU ◽  
XIAORONG PU ◽  
YIDONG SHEN ◽  
ZHANG YI ◽  
XIAOFENG LIAO
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Sum Of Squares ◽  
Clustering Methods ◽  
Clustering Problem ◽  
Mutation Operation ◽  
Iteration Methods ◽  
Genetic Clustering ◽  
The Individual

In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated.

Attention and Focus in Dance

2021 ◽  
Author(s):  
Clare Guss-West
Keyword(s):  
High Performance ◽  
Teaching Practice ◽  
Information Overload ◽  
Critical Role ◽  
Attentional Focus ◽  
Mental Work ◽  
Increased Risk ◽  
Professional Dancers ◽  
And Performance ◽  
Professional Dancer

The Western approach to dance is largely focused on control and mastery of technique, both of which are certainly necessary skills for improving performance. But mindful attention, despite its critical role in high performance, has gotten short shrift—until now. Attention and Focus in Dance, a how-to book rooted in the 20 years of attentional focus findings of researcher Gabriele Wulf, will help dancers unlock their power and stamina reserves, enabling efficient movement, heightening their sensory perception and releasing their dance potential. Author Clare Guss-West—a professional dancer, choreographer, teacher and holistic practitioner—presents a systematic, science-based approach to the mental work of dance. Her approach helps dancers hone the skills of attention, focus and self-cueing to replenish energy and enhance their physical and artistic performance. A Unique, Research-Based Approach Here is what Attention and Focus in Dance offers readers: • A unique approach, connecting the foundations of Eastern movement with Western movement forms • Research-based teaching practices in diverse contexts, including professional dance companies, private studios, and programmes for dancers with special needs or movement challenges • Testimonies and tips from international professional dancers and dance educators who use the book's approach in their training and teaching • A dance-centric focus that can be easily integrated into existing training and teaching practice, in rehearsal, or in rehabilitation contexts to provide immediate and long-term benefits Guss-West explores attentional focus techniques for dancers, teachers and dance health care practitioners, making practical connections between research, movement theory and day-to-day dance practice. “Many dancers are using excessive energy deployment and significant counterproductive effort, and that can lead to a global movement dysfunction, lack of stamina and an increased risk of injury,” says Guss-West. “Attentional focus training is the most relevant study that sport science and Eastern-movement practice can bring to dance.” Book Organisation The text is organised into two parts. Part I guides dancers in looking at the attentional challenges and information overload that many professional dancers suffer from. It outlines the need for a systematic attention and focus strategy, and it explains how scientific research on attentional focus relates to dance practice. This part also examines the ways in which Eastern-movement principles intersect with and complement scientific findings, and it examines how the Eastern and scientific concepts can breathe new life into basic dance elements such as posture, turnout and port de bras. Attention and focus techniques are included for replenishing energy and protecting against energy depletion and exhaustion. Part II presents attention and focus strategies for teaching, self-coaching and cueing. It addresses attentional focus cues for beginners and for more advanced dancers and professionals, and it places attentional focus in the broader context of holistic teaching strategies. Maximising Dance Potential “Whether cueing others or yourself, cueing for high performance is an art,” Guss-West says. “Readers will discover how to format cues and feedback to facilitate effective neuromuscular response and enhance dancer recall of information and accessibility while dancing.” Attention and Focus in Dance offers an abundance of research-backed concepts and inspirational ideas that can help dancers in their learning and performance. This book aids readers in filtering information and directing their focus for optimal physical effect. Ultimately, it guides dancers and teachers in being the best version of themselves and maximising their potential in dance.

scholarly journals Chalcone Synthase: Method Optimization of Extraction and Activity Assay in Strawberry Fruit

2020 ◽  
Author(s):  
Linda Zoungrana ◽  
Alyssa N. Smith ◽  
M. Cecilia do Nascimento Nunes
Keyword(s):  
High Performance ◽  
Method Development ◽  
Chalcone Synthase ◽  
Biological Tissues ◽  
Spectrophotometric Analysis ◽  
Strawberry Fruit ◽  
Activity Assay ◽  
Plant Materials ◽  
Lack Of Information ◽  
Method Optimization

Method development and optimization play a central role in analytical chemistry and more specifically in food biochemistry. When it comes to research, it is common that analytical methods need to be modified to specific experimental biological tissues. While there are several published works on the activity of the enzyme chalcone synthase (CHS) in plant materials, such as sweet basil using ultra- high-performance liquid chromatography, there is a lack of information regarding extraction and activity of CHS in strawberries. Therefore, the main objective of this work was to optimize existing published methods for extraction and activity of CHS in strawberries, using spectrophotometric analysis. It was done through a literature search, a method dissection was performed, followed by theoretical optimization of the protocol, and finally an experimental optimization

scholarly journals RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuan Zhao ◽  
Zhao-Yu Fang ◽  
Cui-Xiang Lin ◽  
Chao Deng ◽  
Yun-Pei Xu ◽  
...  
Keyword(s):  
Random Forest ◽  
Single Cell ◽  
Gene Selection ◽  
Clustering Algorithms ◽  
Selection Methods ◽  
Clustering Methods ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Random Forest Classification ◽  
Forest Classification

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods.

scholarly journals The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?

2021 ◽  
Vol 4 ◽  
Author(s):  
Stefano Markidis
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Performance ◽  
Critical Role ◽  
Low Frequency ◽  
Accurate Solution ◽  
Limiting Factor ◽  
Data Set ◽  
Linear Solvers ◽  
Computational Performance

Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network. PINNs have emerged as a new essential tool to solve various challenging problems, including computing linear systems arising from PDEs, a task for which several traditional methods exist. In this work, we focus first on evaluating the potential of PINNs as linear solvers in the case of the Poisson equation, an omnipresent equation in scientific computing. We characterize PINN linear solvers in terms of accuracy and performance under different network configurations (depth, activation functions, input data set distribution). We highlight the critical role of transfer learning. Our results show that low-frequency components of the solution converge quickly as an effect of the F-principle. In contrast, an accurate solution of the high frequencies requires an exceedingly long time. To address this limitation, we propose integrating PINNs into traditional linear solvers. We show that this integration leads to the development of new solvers whose performance is on par with other high-performance solvers, such as PETSc conjugate gradient linear solvers, in terms of performance and accuracy. Overall, while the accuracy and computational performance are still a limiting factor for the direct use of PINN linear solvers, hybrid strategies combining old traditional linear solver approaches with new emerging deep-learning techniques are among the most promising methods for developing a new class of linear solvers.

scholarly journals Comparison of dimensionality reduction and clustering methods for SARS-CoV-2 genome

2021 ◽  
Vol 10 (4) ◽  
pp. 2170-2180
Author(s):  
Untari N. Wisesty ◽  
Tati Rajab Mengko
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Reduction ◽  
Clustering Algorithm ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Reduction Process ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Clustering Methods

This paper aims to conduct an analysis of the SARS-CoV-2 genome variation was carried out by comparing the results of genome clustering using several clustering algorithms and distribution of sequence in each cluster. The clustering algorithms used are K-means, Gaussian mixture models, agglomerative hierarchical clustering, mean-shift clustering, and DBSCAN. However, the clustering algorithm has a weakness in grouping data that has very high dimensions such as genome data, so that a dimensional reduction process is needed. In this research, dimensionality reduction was carried out using principal component analysis (PCA) and autoencoder method with three models that produce 2, 10, and 50 features. The main contributions achieved were the dimensional reduction and clustering scheme of SARS-CoV-2 sequence data and the performance analysis of each experiment on each scheme and hyper parameters for each method. Based on the results of experiments conducted, PCA and DBSCAN algorithm achieve the highest silhouette score of 0.8770 with three clusters when using two features. However, dimensionality reduction using autoencoder need more iterations to converge. On the testing process with Indonesian sequence data, more than half of them enter one cluster and the rest are distributed in the other two clusters.

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