K-medoids Algorithm Research Articles

SNP genotype calling and quality control for multi-batch-based studies.

In genetic analyses, the term 'batch effect' refers to systematic differences caused by batch heterogeneity. Controlling this unintended effect is the most important step in quality control (QC) processes that precede analyses. Currently, batch effects are not appropriately controlled by statistics, and newer approaches are required. In this report, we propose a new method to detect the heterogeneity of probe intensities among different batches and a procedure for calling genotypes and QC in the presence of a batch effect. First, we conducted a multivariate analysis of variance (MANOVA) to test the differences in probe intensities among batches. If heterogeneity is detected, subjects should be clustered using a K-medoid algorithm using the averages of the probe intensity measurements for each batch and the genotypes of subjects in different clusters should be called separately. The proposed method was used to assess genotyping data of 3619 subjects consisting of 1074 patients with Alzheimer's disease, 296 with mild cognitive impairment (MCI), and 1153 controls. The proposed method improves the accuracy of called genotypes without the need to filter a lot of subjects and SNPs, and therefore is a reasonable approach for controlling batch effects. We proposed a new strategy that detects batch effects with probe intensity measurement and calls genotypes in the presence of batch effects. The application of the proposed method to real data shows that it produces a balanced approach. Furthermore, the proposed method can be extended to various scenarios with a simple modification.

Fault Detection Method Using Multi-mode Principal Component Analysis Based on Gaussian Mixture Model for Sewage Source Heat Pump System

This paper presents an algorithm for fault detection of a sewage heat pump system by designing multi-mode principal component analysis with Gaussian mixture model. If the heat pump system fails, the loss of energy and time is enormous, therefore the fault detection of the system is important. For this purpose, this study proposes a fault detection method using multi-mode principal component analysis with Gaussian mixture model. The data were clustered into multi-mode of Gaussian on principal component subspace. Based on the multi-model, the values of Hotelling’s T2 and SPE were calculated and used for the fault detection as indexes that are compared performance with clustering model using k-means and k-medoids algorithm as well as conventional PCA. Actual data of the sewage heat pump were used to verify the proposed method. The results of the fault detection performance show that the proposed model shows the best performance of fault detection among the conventional, k-means, and k-medoids PCA models.

Application of Machine Learning to Identify Clustering of Cardiometabolic Risk Factors in U.S. Adults.

Aims: The aim of this study is to compare some machine learning methods with traditional statistical parametric analyses using logistic regression to investigate the relationship of risk factors for diabetes and cardiovascular (cardiometabolic risk) for U.S. adults using a cross-sectional data from participants in a wellness improvement program. Methods: Logistic regression was used to find the relationship between individual risk factors, predictor and cardiometabolic risk. Supervised machine learning methods were used to predict risk and produce a ranking of variables' importance. A clustering method was used to identify subpopulations of interest. Predictors were divided into those that are nonmodifiable and those that are modifiable. Results: The population comprised 217,254 adults of whom 8.1% had diabetes. Using logistic regression, six variables were identified to be negatively related and eleven were positively related to cardiometabolic risk. Three supervised machine learning classifiers (random forest, gradient boosting, and bagging) were applied with average AUC to be 0.806. Each classifier also produced a ranking of variables' importance. Four subgroups were identified with a k-medoid clustering algorithm, which were mainly distinguished by gender and diabetes status. Conclusions: The study illustrates that machine learning is an important addition to traditional logistic regression in terms of identifying important cardiometabolic risk factors and ranking their importance and the potential for interventions based on lifestyle and medications at an individual level.

A new clustering and nomenclature for beta turns derived from high-resolution protein structures.

Protein loops connect regular secondary structures and contain 4-residue beta turns which represent 63% of the residues in loops. The commonly used classification of beta turns (Type I, I’, II, II’, VIa1, VIa2, VIb, and VIII) was developed in the 1970s and 1980s from analysis of a small number of proteins of average resolution, and represents only two thirds of beta turns observed in proteins (with a generic class Type IV representing the rest). We present a new clustering of beta-turn conformations from a set of 13,030 turns from 1074 ultra-high resolution protein structures (≤1.2 Å). Our clustering is derived from applying the DBSCAN and k-medoids algorithms to this data set with a metric commonly used in directional statistics applied to the set of dihedral angles from the second and third residues of each turn. We define 18 turn types compared to the 8 classical turn types in common use. We propose a new 2-letter nomenclature for all 18 beta-turn types using Ramachandran region names for the two central residues (e.g., ‘A’ and ‘D’ for alpha regions on the left side of the Ramachandran map and ‘a’ and ‘d’ for equivalent regions on the right-hand side; classical Type I turns are ‘AD’ turns and Type I’ turns are ‘ad’). We identify 11 new types of beta turn, 5 of which are sub-types of classical beta-turn types. Up-to-date statistics, probability densities of conformations, and sequence profiles of beta turns in loops were collected and analyzed. A library of turn types, BetaTurnLib18, and cross-platform software, BetaTurnTool18, which identifies turns in an input protein structure, are freely available and redistributable from dunbrack.fccc.edu/betaturn and github.com/sh-maxim/BetaTurn18. Given the ubiquitous nature of beta turns, this comprehensive study updates understanding of beta turns and should also provide useful tools for protein structure determination, refinement, and prediction programs.

Кластеризация регионов России по уровню развития ипотечного кредитования

In this article, in order to optimize the economic policy in the field of mortgagehousing lending, the clustering of Russian regions by the most optimal method was carried out and analyzed. The main limitations arising from the application of the most popular k-means clustering algorithm for analyzing mortgages are considered and ways to correct them are suggested. The regions were grouped using clustering algorithms using medians and medoids that are more resistant to outliers. A comparison was made of the results of the k-means, k-medians and k-medoids algorithms, and the optimal number of groups of regions with similar indicators in the field of mortgage lending and their relevant regions representatives were found. A hierarchical clustering algorithm based on the Ward method was used, the result of which was the use of five mortgage clusters in Russia. The study of the characteristics of these groups of regions will help in creating a mortgage policy that takes into account the peculiarities of the regions of Russia. All calculations were made in the R programming language; graphics were created in the Rstudio development environment.

Addressing facial dynamics using k-medoids cohort selection algorithm for face recognition

Face recognition is itself a very challenging task and it becomes more challenging when the input images have intra class variations and inter class similarities in a large scale. Yet the recognition accuracy can be improved in some extent by supporting the system with non-matched templates. Therefore a set of cohort images is used in this regard. But all the cohort templates of the initial cohort pool may not be relevant for each and every enrolled subject. So the main focus of this work is to select a subject specific and meaningful cohort subset. This paper proposes a cohort selection method called K-medoids Cohort Selection (KMCS) to select a reference set of non-matched templates which are almost appropriate to the respective subjects. Basically, all cohort scores of a subject are clustered first using K-medoids clustering. Afterward the cluster having more scattered members/scores from its medoid is selected as a cohort subset because this cluster is constituted with the cohorts carrying more discriminative features compared to others. The SIFT points and SURF points are extracted as facial feature. The experiments are conducted on FEI, ORL and Look-alike databases of face images. The matching scores between probe and query images are normalized using T-norm, Max-Min and Aggarwal (Max rule) cohort score normalization techniques before taking the final decision of acceptance or rejection. The results obtained from the experiments show the domination of the proposed system over the non-cohort face recognition system as well as random and Top 10 cohort selection methods. There is another comparative study between k-means and K-medoids clustering for cohort selection.

A cooperative spectrum sensing method based on signal decomposition and K-medoids algorithm

To solve the problem of low sensing performance and low accuracy of threshold estimation in traditional spectrum sensing systems with low signal-to-noise ratio (SNR), we proposes a cooperative spectrum sensing (CSS) method based on signal decomposition and K-medoids clustering algorithm. Firstly, to improve the sensing performance of the system in the case of fewer cooperative secondary users, a feature extraction method based on empirical mode decomposition and matrix decomposition and recombination is proposed. The method can accurately acquire the characteristic information of the sampled signal and improve the feature accuracy. Finally, the features are classified using the K-medoids clustering algorithm. In the experimental part, the result shows that the method can effectively improve the sensing performance of the spectrum sensing system at low SNR.

A cooperative spectrum sensing method based on signal decomposition and K-medoids algorithm

A cooperative spectrum sensing method based on signal decomposition and K-medoids algorithm

A novel approach to text clustering using shift k-medoid

As the amount of data is growing day by day, there is a need to convert it into some effective manner so as to extract some useful information from huge data. Text mining is used to perform this task. In this paper, text clustering is used to convert the large data into different cluster forms to extract the meaningful information for the purpose of analysis so as to get the summarised data. Three partitioning-based clustering techniques, i.e., k-means, k-means fast and k-medoids are compared, and a new algorithm named shift k-medoid is proposed, which is hybrid of k-medoid and mean shift clustering algorithms. Cosine similarity, correlation coefficient and Jaccard similarity measures are used to check the performance of the algorithms and two measures, i.e., randomised feature and normalised mutual information (NMI) feature are used to test the accuracy of the algorithms. The outcomes demonstrate that the best performance is accomplished by using the proposed algorithm.

A novel approach to text clustering using shift k-medoid

As the amount of data is growing day by day, there is a need to convert it into some effective manner so as to extract some useful information from huge data. Text mining is used to perform this task. In this paper, text clustering is used to convert the large data into different cluster forms to extract the meaningful information for the purpose of analysis so as to get the summarised data. Three partitioning-based clustering techniques, i.e., k-means, k-means fast and k-medoids are compared, and a new algorithm named shift k-medoid is proposed, which is hybrid of k-medoid and mean shift clustering algorithms. Cosine similarity, correlation coefficient and Jaccard similarity measures are used to check the performance of the algorithms and two measures, i.e., randomised feature and normalised mutual information (NMI) feature are used to test the accuracy of the algorithms. The outcomes demonstrate that the best performance is accomplished by using the proposed algorithm.

GPOGC: Gaussian Pigeon-Oriented Graph Clustering Algorithm for Social Networks Cluster

As the continuous development of mobile social networks, the structure of the mobile social network increasingly becomes complex. It not only speeds up information transmission between people but also expands the scope of information exchange, which has become an essential and important social media in people’s social life. How to effectively identify and classify these online communities has important practical significance for the study of social networks. Correctly detecting the community structure of mobile social networks can not only improve the accuracy of friend recommendation, link prediction, service user positioning, product marketing, and other aspects but also provide an important basis for the monitoring of online public opinion. But the traditional social network cluster method based on the trust degree mainly calculates the user trust by analyzing the interactive feedback information between users. This method cannot effectively solve the “cold start” problem in the trust calculation process, that is, for the new network node, the trust value of this node cannot be accurately measured due to the lack of interaction with other nodes. Focusing on this problem, we propose a Gaussian pigeon-oriented graph clustering algorithm for social networks’ cluster in this paper. A graph model is first built. Then, an efficient K-medoid algorithm is utilized to search the user center in all groups. The Gaussian pigeon algorithm is used to search the similarity between each user and the central user. Users that meet the similarity threshold are divided into the same user group. Finally, the simulation results show that the proposed method has better cluster effect than other state-of-the-art social networks’ clustering approaches.

Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.

Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients. To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients. We applied three different clustering algorithms-connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm-to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization metrics. Patients enrolled in a national Medicare Advantage plan, categorized in the top decile of spending (n = 6154). Post hoc discriminative models comparing the importance of variables for distinguishing observations in one cluster from the rest. Variance in utilization and spending measures. Connectivity-based, centroid-based, and density-based clustering identified eight, five, and ten subgroups of high-cost patients, respectively. Post hoc discriminative models indicated that density-based clustering subgroups were the most clinically distinct. The variance of utilization and spending measures was the greatest among the subgroups identified through density-based clustering. Machine learning algorithms can be used to segment a high-cost patient population into subgroups of patients that are clinically distinct and associated with meaningful differences in utilization and spending measures. For these purposes, density-based clustering with the OPTICS algorithm outperformed connectivity-based and centroid-based clustering algorithms.

Intra-Cluster Distance Minimization in DNA Methylation Analysis Using an Advanced Tabu-Based Iterative -Medoids Clustering Algorithm (T-CLUST)

Recent advances in DNA methylation profiling have paved the way for understanding the underlying epigenetic mechanisms of various diseases such as cancer. While conventional distance-based clustering algorithms (e.g., hierarchical and k-means clustering) have been heavily used in such profiling owing to their speed in conduct of high-throughput analysis, these methods commonly converge to suboptimal solutions and/or trivial clusters due to their greedy search nature. Hence, methodologies are needed to improve the quality of clusters formed by these algorithms without sacrificing from their speed. In this study, we introduce three related algorithms for a complete high-throughput methylation analysis: a variance-based dimension reduction algorithm to handle high-dimensionality in data, an outlier detection algorithm to identify the outliers of data, and an advanced Tabu-based iterative k-medoids clustering algorithm (T-CLUST) to reduce the impact of initial solutions on the performance of conventional k-medoids algorithm. The performance of the proposed algorithms is demonstrated on nine different real DNA methylation datasets obtained from the Gene Expression Omnibus DataSets database. The accuracy of the cluster identification obtained by our proposed algorithms is higher than those of hierarchical and k-means clustering, as well as the conventional methods. The algorithms are implemented in MATLAB, and available at: http://www.coe.miami.edu/simlab/tclust.html.

The Dynamic Linkage among the Size, Growth and Profitability of Listed Companies in the ASEAN-4 Countries

purpose of this paper is to examine the dynamic relationship among the size, growth, and profitability of listed companies. study sample comprised listed companies in the ASEAN-4 countries—Malaysia, the Philippines, Singapore, and Thailand—over the period 1972-2014. K-medoids algorithm was employed in a cluster analysis, and the generalized method of moments (GMM) was applied to examine the dynamic relationship. empirical results reveal that smaller companies tend to have higher growth than larger companies. Moreover, the results indicate that persistence of growth and persistence of profitability do not exist. There is also evidence that profitability affects companies’ growth, but companies’ growth does not affect profitability. To cite this document: Phassawan Suntraruk, Kanix Bukkavesa, Nat Kulvanich, The Dynamic Linkage among the Size, Growth and Profitability of Listed Companies in the ASEAN-4 Countries, Contemporary Management Research, Vol.14, No.4, pp. 293-310, 2018. Permanent link to this document: http://dx.doi.org/10.7903/cmr.18579

Parallelizing SAT-based de-camouflaging attacks by circuit partitioning and conflict avoiding

As one of the most effective proactive countermeasures against reverse engineering, circuit camouflaging has emerged to be a hot research topic and it is becoming a mature technology with the development of various de-camouflaging attacks. Among them, the SAT-based method is the most powerful one to defeat circuit camouflaging. However, SAT-based attacks have scalability problem due to the complexity of the underlying SAT solvers, and straightforward approach to parallelize SAT-based attacks will fail. In this article, we propose a novel parallelization framework for SAT-based attacks, which consists of a two-level partition method (independent module partitioning and k-medoids clustering), together with a novel conflict avoidance strategy. Specifically, we first break down the camouflaged netlist into multiple independent modules that can be solved in parallel. However, any good circuit camouflaging approach should produce one or multiple large modules. To solve this problem, we further apply the k-medoids algorithm to partition the large modules into multiple “high cohesion” and “low coupling” clusters. Utilizing the relative independence of these clusters, we propose a two-stage attack method to avoid conflicts among clusters. Experimental results on OpenSparc T1 microprocessor controller demonstrate that our approach can on average reduce the scales of the SAT formulas by more than 50%, reduce the attack iteration number by 45%, and achieves on average 3.6× and maximum 10× speed up over the best-known SAT-based de-camouflaging tool.

Genetic algorithm for assigning weights to gene expressions using functional annotations

A method, named genetic algorithm for assigning weights to gene expressions using functional annotations (GAAWGEFA), is developed to assign proper weights to the gene expressions at each time point. The weights are estimated using functional annotations of the genes in a genetic algorithm framework. The method shows gene similarity in an improved manner as compared with other existing methods because it takes advantage of the existing functional annotations of the genes. The weight combination for the expressions at different time points is determined by maximizing the fitness function of GAAWGEFA in terms of the positive predictive value (PPV) for the top 10,000 gene pairs. The performance of the proposed method is primarily compared with Biweight mid correlation (BICOR) and original expression values for the six Saccharomyces cerevisiae datasets and one Bacillus subtilis dataset. The utility of GAAWGEFA is shown in predicting the functions of 48 unclassified genes (using p-value cutoff 10−13) from Saccharomyces cerevisiae microarray data where the expressions are weighted using GAAWGEFA and are clustered using k-medoids algorithm. The related code along with various parameters is available at http://sampa.droppages.com/GAAWGEFA.html.

Construction of a stable vehicular ad hoc network based on hybrid genetic algorithm

In vehicular ad hoc networks, the vehicle speed can exceed 120 kmph. Therefore, any node can enter or leave the network within a very short time. This mobility adversely affects the network connectivity and decreases the life time of all established links. To overcome these problems, many routing protocols based clustering technique have been proposed. Indeed, the poor assignment of vehicles to clusters is the most important shortcomings where an inaccurate affiliation may reduce the algorithm’s effectiveness and disrupt all results and analyzes. Therefore, in this paper, we used a hybrid genetic algorithm to improve the cluster maintenance phase in our Weighted K-medoid Clustering Algorithm (WKCA) proposed recently. The proposed model incorporated the tabu search within genetic algorithm to allow the scan of all search space and to reach the best solution without falling into the local optima. This model improves the assignment of nodes to clusters, which in turn achieves efficient vehicle communication and ensures more stability in clustered architecture. Based on relevant metrics, the results obtained in a simulation game show that the enhanced model (E-WKCA) achieves more stability and robustness when compared to the original algorithm (WKCA) and other approaches designed for the same objective.

Spectral clustering based approach for controller placement problem in software defined networking

In a large-scale SDN, the network was effectively managed by deploying multiple controllers. In order to improve reliability and reduce delay in the existing multi-controller deployment scheme, we proposed a Multi-Controllers Elastic Placement (MCEP) algorithm based on reliability and delay. First, the multi-controller deployment problem was transformed into the row vector classification problem of the network topology matrix based on spectral clustering. Then, the k-medoids algorithm based on simulated annealing was used to classify the row vectors to achieve the flexible deployment of multiple controllers. The simulation results show that, compared with the existing algorithms, while guaranteeing low delay, the reliability of control paths increase by 17% on average.

K-Medoids Clustering of Data Sequences With Composite Distributions

This paper studies clustering of data sequences using the k-medoids algorithm. All the data sequences are assumed to be generated from \emph{unknown} continuous distributions, which form clusters with each cluster containing a composite set of closely located distributions (based on a certain distance metric between distributions). The maximum intra-cluster distance is assumed to be smaller than the minimum inter-cluster distance, and both values are assumed to be known. The goal is to group the data sequences together if their underlying generative distributions (which are unknown) belong to one cluster. Distribution distance metrics based k-medoids algorithms are proposed for known and unknown number of distribution clusters. Upper bounds on the error probability and convergence results in the large sample regime are also provided. It is shown that the error probability decays exponentially fast as the number of samples in each data sequence goes to infinity. The error exponent has a simple form regardless of the distance metric applied when certain conditions are satisfied. In particular, the error exponent is characterized when either the Kolmogrov-Smirnov distance or the maximum mean discrepancy are used as the distance metric. Simulation results are provided to validate the analysis.

Fluid intake patterns of children and adolescents: results of six Liq.In7 national cross-sectional surveys

PurposeThis study aimed to identify and characterize patterns of fluid intake in children and adolescents from six countries: Argentina, Brazil, China, Indonesia, Mexico and Uruguay.MethodsData on fluid intake volume and type amongst children (4–9 years; N = 1400) and adolescents (10–17 years; N = 1781) were collected using the validated 7-day fluid-specific record (Liq.In7 record). To identify relatively distinct clusters of subjects based on eight fluid types (water, milk and its derivatives, hot beverages, sugar-sweetened beverages (SSB), 100% fruit juices, artificial/non-nutritive sweetened beverages, alcoholic beverages, other beverages), a cluster analysis (partitioning around k-medoids algorithm) was used. Clusters were then characterized according to their socio-demographics and lifestyle indicators.ResultsThe six interpretable clusters identified were: low drinkers–SSB (n 523), low drinkers–water and milk (n 615), medium mixed drinkers (n 914), high drinkers–SSB (n 513), high drinkers–water (n 352) and very high drinkers–water (n 264). Country of residence was the dominant characteristic, followed by socioeconomic level, in all six patterns.ConclusionsThis analysis showed that consumption of water and SSB were the primary drivers of the clusters. In addition to country, socio-demographic and lifestyle factors played a role in determining the characteristics of each cluster. This information highlights the need to target interventions in particular populations aimed at changing fluid intake behavior and improving health in children and adolescents.