Статті в журналах: "Overlapping Clusters"

1

Mirzaie, Mansooreh, Ahmad Barani, Naser Nematbakkhsh, and Majid Mohammad-Beigi. "Bayesian-OverDBC: A Bayesian Density-Based Approach for Modeling Overlapping Clusters." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/187053.

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Although most research in density-based clustering algorithms focused on finding distinct clusters, many real-world applications (such as gene functions in a gene regulatory network) have inherently overlapping clusters. Even with overlapping features, density-based clustering methods do not define a probabilistic model of data. Therefore, it is hard to determine how “good” clustering, predicting, and clustering new data into existing clusters are. Therefore, a probability model for overlap density-based clustering is a critical need for large data analysis. In this paper, a new Bayesian density-based method (Bayesian-OverDBC) for modeling the overlapping clusters is presented. Bayesian-OverDBC can predict the formation of a new cluster. It can also predict the overlapping of cluster with existing clusters. Bayesian-OverDBC has been compared with other algorithms (nonoverlapping and overlapping models). The results show that Bayesian-OverDBC can be significantly better than other methods in analyzing microarray data.

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2

Singh, Sukhminder. "Estimation in overlapping clusters." Communications in Statistics - Theory and Methods 17, no. 2 (January 1988): 613–21. http://dx.doi.org/10.1080/03610928808829643.

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3

Danganan, Alvincent Egonia, Ariel M. Sison, and Ruji P. Medina. "OCA: overlapping clustering application unsupervised approach for data analysis." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 3 (June 1, 2019): 1471. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1471-1478.

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<p>In this paper, a new data analysis tool called Overlapping Clustering Application (OCA) was presented. It was developed to identify overlapping clusters and outliers in an unsupervised manner. The main function of OCA is composed of three phases. The first phase is the detection of the abnormal values(outliers) in the datasets using median absolute deviation. The second phase is to segment data objects into cluster using k-means algorithm. Finally, the last phase is the identification of overlapping clusters, it uses maxdist (maximum distance of data objects allowed in a cluster) as a predictor of data objects that can belong to multiple clusters. Experimental results revealed that the developed OCA proved its capability in detecting overlapping clusters and outliers accordingly.</p>

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4

Danganan, Alvincent E., and Edjie Malonzo De Los Reyes. "eHMCOKE: an enhanced overlapping clustering algorithm for data analysis." Bulletin of Electrical Engineering and Informatics 10, no. 4 (August 1, 2021): 2212–22. http://dx.doi.org/10.11591/eei.v10i4.2547.

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Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) in detecting outliers in datasets makes the algorithm more effective with regards to overlapping clustering. Nevertheless, analysis of the applied MAD positioning was not considered. In this paper, the incorporation of MAD used to detect outliers in the datasets was analyzed to determine the appropriate position in identifying the outlier before applying it in the clustering application. And the assumption of the study was the size of the cluster and cluster that are close to each other can led to a higher runtime performance in terms of overlapping clusters. Therefore, additional parameters such as radius of clusters and distance between clusters are added measurements in the algorithm procedures. Evaluation was done through experimentations using synthetic and real datasets. The performance of the eHMCOKE was evaluated via F1-measure criterion, speed and percentage of improvement. Evaluation results revealed that the eHMCOKE takes less time to discover overlap clusters with an improvement rate of 22% and achieved the best performance of 91.5% accuracy rate via F1-measure in identifying overlapping clusters over the IMCOKE algorithm. These results proved that the eHMCOKE significantly outruns the IMCOKE algorithm on mosts of the test conducted.

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5

Qing, Huan. "Studying Asymmetric Structure in Directed Networks by Overlapping and Non-Overlapping Models." Entropy 24, no. 9 (August 30, 2022): 1216. http://dx.doi.org/10.3390/e24091216.

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We consider the problem of modeling and estimating communities in directed networks. Models to this problem in the previous literature always assume that the sending clusters and the receiving clusters have non-overlapping property or overlapping property simultaneously. However, previous models cannot model the directed network in which nodes in sending clusters have overlapping property, while nodes in receiving clusters have non-overlapping property, especially for the case when the number of sending clusters is no larger than that of the receiving clusters. This kind of directed network exists in the real world for its randomness, and by the fact that we have little prior knowledge of the community structure for some real-world directed networks. To study the asymmetric structure for such directed networks, we propose a flexible and identifiable Overlapping and Non-overlapping model (ONM). We also provide one model as an extension of ONM to model the directed network, with a variation in node degree. Two spectral clustering algorithms are designed to fit the models. We establish a theoretical guarantee on the estimation consistency for the algorithms under the proposed models. A small scale computer-generated directed networks are designed and conducted to support our theoretical results. Four real-world directed networks are used to illustrate the algorithms, and the results reveal the existence of highly mixed nodes and the asymmetric structure for these networks.

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6

Vidojević, Filip, Dušan Džamić, and Miroslav Marić. "E-function for Fuzzy Clustering in Complex Networks." Ipsi Transactions on Internet research 18, no. 1 (January 1, 2022): 17–21. http://dx.doi.org/10.58245/ipsi.tir.22jr.04.

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In many real-life situations, data consists of entities and the connections between them, which are naturally described by a complex network (graph). The structure of the network is often such that it is possible to group nodes based on the existence of connections between them, where such groups are called clusters (communities, modules). If the nodes are allowed to partially belong to clusters, they are called fuzzy (overlapping) clusters. There is a huge number of algorithms in the literature that perform fuzzy clustering, that is finds overlapping clusters, so a mechanism is needed to evaluate such clustering. The function that assesses the quality of a performed clustering is called the cluster quality function. One of the latest proposed quality functions is the E-function. The E-function is based on a comparison of the internal structure of a cluster, i.e., the connection between nodes within a cluster and the connection of its nodes with the nodes of other clusters. Due to its exponential nature, the E-function is sensitive to small changes in the membership degrees to which the nodes belong to clusters. As such, it has shown good results in evaluating clustering on known data sets. In this paper, the experimental results that the modified E-function achieves in the case of overlapping clusters are presented. Also, some possibilities for fuzzy clustering by optimizing the E-function are displayed.

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7

Wu, Mary, Byung Chul Ahn, and Chong Gun Kim. "A Channel Reuse Procedure in Clustering Sensor Networks." Applied Mechanics and Materials 284-287 (January 2013): 1981–85. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1981.

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Sensor nodes having the limited resource, energy efficiency is an important issue. Clustering on the sensor networks reduces the volume of inter-node communications and raises energy efficiency by transmitting the data collected from members by a cluster head to a sink node. But, due to radio frequency characteristics, interference and collision can occur between neighbor clusters, the resulted re-transmission is more energy consuming. The interference and collision occurred among adjacent clusters can be resolved by assigning non-overlapping channels among neighbor clusters. In this paper, we propose a channel reuse procedure which shows practical steps to assign dynamically channels among adjacent clusters in sensor networks. This method is expected to perform successfully the allocation process of non-overlapping channels for various cluster topologies.

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8

Alaqtash, Mohammad, Moayad A.Fadhil, and Ali F. Al-Azzawi. "A Modified Overlapping Partitioning Clustering Algorithm for Categorical Data Clustering." Bulletin of Electrical Engineering and Informatics 7, no. 1 (March 1, 2018): 55–62. http://dx.doi.org/10.11591/eei.v7i1.896.

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Clustering is one of the important approaches for Clustering enables the grouping of unlabeled data by partitioning data into clusters with similar patterns. Over the past decades, many clustering algorithms have been developed for various clustering problems. An overlapping partitioning clustering (OPC) algorithm can only handle numerical data. Hence, novel clustering algorithms have been studied extensively to overcome this issue. By increasing the number of objects belonging to one cluster and distance between cluster centers, the study aimed to cluster the textual data type without losing the main functions. The proposed study herein included over twenty newsgroup dataset, which consisted of approximately 20000 textual documents. By introducing some modifications to the traditional algorithm, an acceptable level of homogeneity and completeness of clusters were generated. Modifications were performed on the pre-processing phase and data representation, along with the number methods which influence the primary function of the algorithm. Subsequently, the results were evaluated and compared with the k-means algorithm of the training and test datasets. The results indicated that the modified algorithm could successfully handle the categorical data and produce satisfactory clusters.

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9

Lee, Kyung-Soon. "Resampling Feedback Documents Using Overlapping Clusters." KIPS Transactions:PartB 16B, no. 3 (June 30, 2009): 247–56. http://dx.doi.org/10.3745/kipstb.2009.16-b.3.247.

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10

Amdekar, S. J. "An Unbiased Estimator in Overlapping Clusters." Calcutta Statistical Association Bulletin 34, no. 3-4 (September 1985): 231–32. http://dx.doi.org/10.1177/0008068319850312.

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11

Reiss, Howard, and Richard K. Bowles. "Mapping volume scale for overlapping clusters." Journal of Chemical Physics 112, no. 3 (January 15, 2000): 1390–94. http://dx.doi.org/10.1063/1.480692.

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12

Danganan, Alvincent E., and Regina P. Arceo. "Overlapping clustering with k-median extension algorithm: An effective approach for overlapping clustering." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (June 1, 2022): 1607. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1607-1615.

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Most natural <span>world data involves overlapping communities where an object may belong to one or more clusters, referred to as overlapping clustering. However, it is worth mentioning that these algorithms have a significant drawback. Since some of the algorithm uses k-means, it also inherits the characteristics of being noise sensitive due to the arithmetic mean value which noisy data can considerably influence and affects the clustering algorithm by biasing the structure of obtained clusters. This paper proposed a new overlapping clustering algorithm named OCKMEx, which uses k-median to identify overlapping clusters in the presence of outliers. This new method aims to determine the insensitivity of the OCKMEx algorithm in locating data points that overlap even with outliers. An experimental evaluation of the algorithm was conducted wherein synthetic datasets served as a data source, and the F1 measure criterion was applied to assess the OCKMEx algorithm performance. Results indicate that the OCKMEx algorithm implementing the use of k-median performed a higher accuracy rate of 100% in identifying data points that overlap even with outliers compared to the existing k-means algorithm. The algorithm exhibited a promising performance in identifying overlapping clusters and was resistant to outliers.</span>

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13

Faccioli, L., F. Pacaud, J. L. Sauvageot, M. Pierre, L. Chiappetti, N. Clerc, R. Gastaud, E. Koulouridis, A. M. C. Le Brun, and A. Valotti. "The XXL Survey." Astronomy & Astrophysics 620 (November 20, 2018): A9. http://dx.doi.org/10.1051/0004-6361/201832931.

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Aims. A well characterised detection pipeline is an important ingredient for X-ray cluster surveys. Methods. We present the final development of the XXL Survey pipeline. The pipeline optimally uses X-ray information by combining many overlapping observations of a source when possible, both for its detection and its characterisation. It can robustly detect and characterise several types of X-ray sources: AGNs (point-like), galaxy clusters (extended), galaxy clusters contaminated by a central AGN, and pairs of AGNs close on the sky. We perform a thorough suite of validation tests via realistic simulations of XMM-Newton images and we introduce new selection criteria for various types of sources that will be detected by the survey. Results. We find that the use of overlapping observations allows new clusters to be securely identified that would be missed or less securely identified by using only one observation at a time. We also find that, with the new pipeline we can robustly identify clusters with a central AGN that would otherwise have been missed, and we can flag pairs of AGNs close on the sky that might have been mistaken for a cluster.

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14

Chen, Yi-Hui, Eric Jui-Lin Lu, and Ya-Wen Cheng. "Categorization of Multiple Documents Using Fuzzy Overlapping Clustering Based on Formal Concept Analysis." International Journal of Software Engineering and Knowledge Engineering 30, no. 05 (May 2020): 631–47. http://dx.doi.org/10.1142/s0218194020500229.

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Most clustering algorithms build disjoint clusters. However, clusters might be overlapped because documents may belong to two or more categories in the real world. For example, a paper discussing the Apple Watch may be categorized into either 3C, Fashion, or even Clothing and Shoes. Therefore, overlapping clustering algorithms have been studied such that a resource can be assigned to one or more clusters. Formal Concept Analysis (FCA), which has many practical applications in information science, has been used in disjoin clustering, but has not been studied in overlapping clustering. To make overlapping clustering possible by using FCA, we propose an approach, including two types of transformation. From the experimental results, it shows that the proposed fuzzy overlapping clustering performed more efficiently than existing overlapping clustering methods. The positive results confirm the feasibility of the proposed scheme used in overlapping clustering. Also, it can be used in applications such as recommendation systems.

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15

Virmani, Deepali, Nikita Jain, Ketan Parikh, Shefali Upadhyaya, and Abhishek Srivastav. "Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology." International Journal of Knowledge Discovery in Bioinformatics 8, no. 1 (January 2018): 42–59. http://dx.doi.org/10.4018/ijkdb.2018010104.

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This article describes how data is relevant and if it can be organized, linked with other data and grouped into a cluster. Clustering is the process of organizing a given set of objects into a set of disjoint groups called clusters. There are a number of clustering algorithms like k-means, k-medoids, normalized k-means, etc. So, the focus remains on efficiency and accuracy of algorithms. The focus is also on the time it takes for clustering and reducing overlapping between clusters. K-means is one of the simplest unsupervised learning algorithms that solves the well-known clustering problem. The k-means algorithm partitions data into K clusters and the centroids are randomly chosen resulting numeric values prohibits it from being used to cluster real world data containing categorical values. Poor selection of initial centroids can result in poor clustering. This article deals with a proposed algorithm which is a variant of k-means with some modifications resulting in better clustering, reduced overlapping and lesser time required for clustering by selecting initial centres in k-means and normalizing the data.

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Pérez-Suárez, Airel, José Fco Martínez-Trinidad, Jesús A. Carrasco-Ochoa, and José E. Medina-Pagola. "A dynamic clustering algorithm for building overlapping clusters." Intelligent Data Analysis 16, no. 2 (March 1, 2012): 211–32. http://dx.doi.org/10.3233/ida-2012-0520.

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17

Lee, Woojung, and JoyceJiyoung Whang. "Cascading Behavior and Information Diffusion in Overlapping Clusters." Journal of KIISE 47, no. 4 (April 30, 2020): 422–32. http://dx.doi.org/10.5626/jok.2020.47.4.422.

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18

Balaguer-Núñez, L., M. López del Fresno, E. Solano, D. Galadí-Enríquez, C. Jordi, F. Jimenez-Esteban, E. Masana, J. Carbajo-Hijarrubia, and E. Paunzen. "Clusterix 2.0: a virtual observatory tool to estimate cluster membership probability." Monthly Notices of the Royal Astronomical Society 492, no. 4 (February 11, 2020): 5811–43. http://dx.doi.org/10.1093/mnras/stz3610.

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ABSTRACT Clusterix 2.0 is a web-based, Virtual Observatory compliant, interactive tool for the determination of membership probabilities in stellar clusters based on proper-motion data using a fully non-parametric method. In an area occupied by a cluster, the frequency function is made up of two contributions: cluster and field stars. The tool performs an empirical determination of the frequency functions from the vector point diagram without relying on any previous assumption about their profiles. Clusterix 2.0 allows us to search the appropriate spatial areas in an interactive way until an optimal separation of the two populations is obtained. Several parameters can be adjusted to make the calculation computationally feasible without interfering with the quality of the results. The system offers the possibility to query different catalogues, such as Gaia, or upload a user’s own data. The results of the membership determination can be sent via Simple Application Messaging Protocol (SAMP) to Virtual Observatory (VO) tools such as Tool for OPerations on Catalogues And Tables (TOPCAT). We apply Clusterix 2.0 to several open clusters with different properties and environments to show the capabilities of the tool: an area of five degrees radius around NGC 2682 (M67), an old, well-known cluster; a young cluster NGC 2516 with a striking elongated structure extended up to four degrees; NGC 1750 and NGC 1758, a pair of partly overlapping clusters; the area of NGC 1817, where we confirm a little-known cluster, Juchert 23; and an area with many clusters, where we disentangle two overlapping clusters situated where only one was previously known: Ruprecht 26 and the new Clusterix 1.

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Chambers, Angela, David Atkinson, and Fiona Farr. "Centre for Applied Language Studies, University of Limerick, Ireland." Language Teaching 48, no. 2 (March 13, 2015): 288–92. http://dx.doi.org/10.1017/s0261444814000445.

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The Centre for Applied Language Studies (CALS), founded in 1997, brings together researchers and postgraduate students from several disciplines in language studies, and is structured in three research clusters: New learning environments; Discourse, society and identity; and Plurilingualism and language policy. There is a certain amount of overlapping between the clusters, and several researchers are active in more than one cluster. Thus research in language teacher education is present both in the New learning environments cluster and in Discourse, society and identity. Corpus-based methodologies are also prominent in both these clusters. Furthermore, discourse analysis is present as a methodology in all three. The following sections provide information on current research and selected research outcomes within the three research clusters from 2011 to 2013.

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20

Kyoya, Shunki, and Kenji Yamanishi. "Summarizing Finite Mixture Model with Overlapping Quantification." Entropy 23, no. 11 (November 13, 2021): 1503. http://dx.doi.org/10.3390/e23111503.

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Finite mixture models are widely used for modeling and clustering data. When they are used for clustering, they are often interpreted by regarding each component as one cluster. However, this assumption may be invalid when the components overlap. It leads to the issue of analyzing such overlaps to correctly understand the models. The primary purpose of this paper is to establish a theoretical framework for interpreting the overlapping mixture models by estimating how they overlap, using measures of information such as entropy and mutual information. This is achieved by merging components to regard multiple components as one cluster and summarizing the merging results. First, we propose three conditions that any merging criterion should satisfy. Then, we investigate whether several existing merging criteria satisfy the conditions and modify them to fulfill more conditions. Second, we propose a novel concept named clustering summarization to evaluate the merging results. In it, we can quantify how overlapped and biased the clusters are, using mutual information-based criteria. Using artificial and real datasets, we empirically demonstrate that our methods of modifying criteria and summarizing results are effective for understanding the cluster structures. We therefore give a new view of interpretability/explainability for model-based clustering.

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21

Azizah, Anestasya Nur, Tatik Widiharih, and Arief Rachman Hakim. "Kernel K-Means Clustering untuk Pengelompokan Sungai di Kota Semarang Berdasarkan Faktor Pencemaran Air." Jurnal Gaussian 11, no. 2 (August 28, 2022): 228–36. http://dx.doi.org/10.14710/j.gauss.v11i2.35470.

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K-Means Clustering is one of the types of non-hierarchical cluster analysis which is frequently used, but has a weakness in processing data with non-linearly separable (do not have clear boundaries) characteristic and overlapping cluster, that is when visually the results of a cluster are between other clusters. The Gaussian Kernel Function in Kernel K-Means Clustering can be used to solve data with non-linearly separable characteristic and overlapping cluster. The difference between Kernel K-Means Clustering and K-Means lies on the input data that have to be plotted in a new dimension using kernel function. The real data used are the data of 47 rivers and 18 indicators of river water pollution from Dinas Lingkungan Hidup (DLH) of Semarang City in the first semester of 2019. The cluster results evaluation is used the Calinski-Harabasz, Silhouette, and Xie-Beni indexes. The goals of this study are to know the step concepts and analysis results of Kernel K-Means Clustering for the grouping of rivers in Semarang City based on water pollution factors. Based on the results of the study, the cluster results evaluation show that the best number of clusters K=4

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22

Peng, Yun, Shengyi Zhao, and Jizhan Liu. "Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method." Electronics 10, no. 22 (November 16, 2021): 2813. http://dx.doi.org/10.3390/electronics10222813.

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Accurately extracting the grape cluster at the front of overlapping grape clusters is the primary problem of the grape-harvesting robot. To solve the difficult problem of identifying and segmenting the overlapping grape clusters in the cultivation environment of a trellis, a simple method based on the deep learning network and the idea of region growing is proposed. Firstly, the region of grape in an RGB image was obtained by the finely trained DeepLabV3+ model. The idea of transfer learning was adopted when training the network with a limited number of training sets. Then, the corresponding region of the grape in the depth image captured by RealSense D435 was processed by the proposed depth region growing algorithm (DRG) to extract the front cluster. The depth region growing method uses the depth value instead of gray value to achieve clustering. Finally, it fils the holes in the clustered region of interest, extracts the contours, and maps the obtained contours to the RGB image. The images captured by RealSense D435 in a natural trellis environment were adopted to evaluate the performance of the proposed method. The experimental results showed that the recall and precision of the proposed method were 89.2% and 87.5%, respectively. The demonstrated performance indicated that the proposed method could satisfy the requirements of practical application for robotic grape harvesting.

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23

Fang, Changjian, Dejun Mu, Zhenghong Deng, Jun Hu, and Chen-He Yi. "Fast detection of the fuzzy communities based on leader-driven algorithm." International Journal of Modern Physics B 32, no. 06 (February 26, 2018): 1850058. http://dx.doi.org/10.1142/s0217979218500583.

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In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of clusters can be learned using only the network structure. The LDA is also an extremely fast algorithm, having runtime linear in the network size. Thus, this algorithm can be used to efficiently cluster extremely large networks.

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24

Liu, Naiyuan, Dehao Shao, Jiaqi Tan, Qianqian Wan, and Tianyu Zhou. "Study of MLP-based classification of multi-cluster TPC signals." Theoretical and Natural Science 5, no. 1 (May 25, 2023): 629–39. http://dx.doi.org/10.54254/2753-8818/5/20230405.

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This study focuses on the classification of multi-cluster events based on a parameterization of data from a time projection chamber using machine learning. Samples containing a mixture of single and overlapping two-cluster events, both in one and two dimensions, were studied using multi-layer perceptrons and other MVA algorithms provided in the Scikit-learn package. The classification was based on various sets of features and classification accuracies of up to 97% for 1D clusters and 97% for 2D clusters were obtained. This study demonstrates that the efficient classification of signals for further processing through machine learning is feasible and efficient.

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25

Ciupala, Laura, Adrian DEACONU, and Delia SPRIDON. "ALGORITHM FOR MERGING AND INTERPOLATING CLUSTERS IN OVERLAPPING IMAGES." SERIES III - MATEMATICS, INFORMATICS, PHYSICS 13(62), no. 2 (January 20, 2021): 697–704. http://dx.doi.org/10.31926/but.mif.2020.13.62.2.25.

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An image overlapping algorithm, taking into account certain properties of objects identified in the images (average intensity, movement speed, etc) is proposed. The algorithm minimizes both memory and time complexity and it can be used in various applications, especially in medical imaging analysis. The idea behind the proposed algorithm is surface merging and interpolation.

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26

Khandekar, Rohit, Guy Kortsarz, and Vahab Mirrokni. "On the Advantage of Overlapping Clusters for Minimizing Conductance." Algorithmica 69, no. 4 (March 6, 2013): 844–63. http://dx.doi.org/10.1007/s00453-013-9761-8.

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27

Galadí-Enríquez, D., C. Jordi, E. Trullols, J. Guibert, K. P. Tian, and J. L. Zhao. "The overlapping open clusters NGC 1750 and NGC 1758." Astronomy and Astrophysics Supplement Series 131, no. 2 (August 1998): 239–58. http://dx.doi.org/10.1051/aas:1998433.

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28

Kobayashi, Mei, and Masaki Aono. "Exploring overlapping clusters using dynamic re-scaling and sampling." Knowledge and Information Systems 10, no. 3 (March 30, 2006): 295–313. http://dx.doi.org/10.1007/s10115-006-0005-y.

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29

Osahan, Sukhminder S. "Overlapping Clusters of Tuberculosis Contacts: An Improved Sampling Strategy." Biometrical Journal 39, no. 6 (1997): 689–97. http://dx.doi.org/10.1002/bimj.4710390607.

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30

Aguena, M., C. Benoist, L. N. da Costa, R. L. C. Ogando, J. Gschwend, H. B. Sampaio-Santos, M. Lima, et al. "The WaZP galaxy cluster sample of the dark energy survey year 1." Monthly Notices of the Royal Astronomical Society 502, no. 3 (February 16, 2021): 4435–56. http://dx.doi.org/10.1093/mnras/stab264.

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ABSTRACT We present a new (2+1)D galaxy cluster finder based on photometric redshifts called Wavelet Z Photometric (WaZP) applied to DES first year (Y1A1) data. The results are compared to clusters detected by the South Pole Telescope (SPT) survey and the redMaPPer cluster finder, the latter based on the same photometric data. WaZP searches for clusters in wavelet-based density maps of galaxies selected in photometric redshift space without any assumption on the cluster galaxy populations. The comparison to other cluster samples was performed with a matching algorithm based on angular proximity and redshift difference of the clusters. It led to the development of a new approach to match two optical cluster samples, following an iterative approach to minimize incorrect associations. The WaZP cluster finder applied to DES Y1A1 galaxy survey (1511.13 deg2 up to mi = 23 mag) led to the detection of 60 547 galaxy clusters with redshifts 0.05 < z < 0.9 and richness Ngals ≥ 5. Considering the overlapping regions and redshift ranges between the DES Y1A1 and SPT cluster surveys, all sz based SPT clusters are recovered by the WaZP sample. The comparison between WaZP and redMaPPer cluster samples showed an excellent overall agreement for clusters with richness Ngals (λ for redMaPPer) greater than 25 (20), with 95 per cent recovery on both directions. Based on the cluster cross-match, we explore the relative fragmentation of the two cluster samples and investigate the possible signatures of unmatched clusters.

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Lai, Daphne Teck Ching, and Yuji Sato. "An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †." Algorithms 14, no. 11 (November 22, 2021): 338. http://dx.doi.org/10.3390/a14110338.

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Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.

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Fu, Xiaolan. "Research on Financial Data Query and Distribution Scheme Based on SQL Database." Wireless Communications and Mobile Computing 2020 (November 26, 2020): 1–7. http://dx.doi.org/10.1155/2020/8819083.

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With the advance of optimization and merger colleges and universities, a university often contains more than one campus. The traditional centralized education management system has been unable to meet the needs of use. The model detects the intrusion by dividing the clusters in the clustering result into normal clusters and abnormal clusters and analyzing the weighted average density of object x to be detected in each cluster and the weighted overlapping distance of x and each centre point. We verified the intrusion detection performance of the model on the KDD Cup 99 dataset. The experimental results show that the model established in this paper has certain theoretical value.

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Al-Sabaawi, Ali M. Ahmed, Hacer Karacan, and Yusuf Erkan Yenice. "A Novel Overlapping Method to Alleviate the Cold-Start Problem in Recommendation Systems." International Journal of Software Engineering and Knowledge Engineering 31, no. 09 (September 2021): 1277–97. http://dx.doi.org/10.1142/s0218194021500418.

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Recommendation systems (RSs) are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The main objective of RSs is to tool up users with desired items that meet their preferences. A major problem in RSs is called: “cold-start”; it is a potential problem called so in computer-based information systems which comprises a degree of automated data modeling. Particularly, it concerns the issue in which the system cannot draw any inferences nor have it yet gathered sufficient information about users or items. Since RSs performance is substantially limited by cold-start users and cold-start items problems; this research study takes the route for a major aim to attenuate users’ cold-start problem. Still in the process of researching, sundry studies have been conducted to tackle this issue by using clustering techniques to group users according to their social relations, their ratings or both. However, a clustering technique disregards a variety of users’ tastes. In this case, the researcher has adopted the overlapping technique as a tool to deal with the clustering technique’s defects. The advantage of the overlapping technique excels over others by allowing users to belong to multi-clusters at the same time according to their behavior in the social network and ratings feedback. On that account, a novel overlapping method is presented and applied. This latter is executed by using the partitioning around medoids (PAM) algorithm to implement the clustering, which is achieved by means of exploiting social relations and confidence values. After acquiring users’ clusters, the average distances are computed in each cluster. Thereafter, a content comparison is made regarding the distances between every user and the computed distances of the clusters. If the comparison result is less than or equal to the average distance of a cluster, a new user is added to this cluster. The singular value decomposition plus (SVD[Formula: see text]) method is then applied to every cluster to compute predictions values. The outcome is calculated by computing the average of mean absolute error (MAE) and root mean square error (RMSE) for every cluster. The model is tested by two real world datasets: Ciao and FilmTrust. Ultimately, findings have exhibited a great deal of insights on how the proposed model outperformed a number of the state-of-the-art studies in terms of prediction accuracy.

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Zhang, Jian, and Ling Shen. "An Improved Fuzzyc-Means Clustering Algorithm Based on Shadowed Sets and PSO." Computational Intelligence and Neuroscience 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/368628.

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To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzyc-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.

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Kimani-Njogu, Susan W., William A. Overholt, James Woolley, and Annette Walker. "Biosystematics of the Cotesia flavipes species complex (Hymenoptera: Braconidae): morphometrics of selected allopatric populations." Bulletin of Entomological Research 87, no. 1 (February 1997): 61–66. http://dx.doi.org/10.1017/s0007485300036361.

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AbstractMorphometric studies of allopatric populations of the Cotesia flavipes species complex representing three putative species; C. flavipes Cameron, C. sesamiae (Cameron) and C. chilonis (Matsumura), were conducted. Sixteen characters were measured. Principal component analysis separated the complex into three somewhat overlapping groups that corresponded well with previous concepts of the species. Canonical variate analysis separated the complex into three distinct clusters with populations from Africa together, populations from Asia and the Neotropics forming a second cluster, and material from China and Japan forming a third cluster. The Mahalanobis squared distances between the three clusters were nearly equal. Results support recognition of three species in the C. flavipes complex.

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Yu, Linsheng, Fang Liu, Sisi Huang, Shoudong Bi, Chao Zong, and Tianshu Wang. "Morphmetric analysis of apis cerana populations in Huangshan, China." Journal of Apicultural Science 57, no. 2 (December 1, 2013): 117–24. http://dx.doi.org/10.2478/jas-2013-0022.

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Abstract Honey bees (Apis cerana Fabricius) were collected from 195 colonies at seven different localities spanning the main beekeeping areas in Huangshan. Morphometric methods were used to measure seven standard morphometric characters, and these bees were compared to samples from the Henan, Shandong, and Yunnan provinces. Principal component analysis of the total Huangshan database yielded two clusters: bees from Jinxian and Jixixian, and those from other localities. Within the latter cluster, discriminant and hierarchical cluster analyses revealed overlapping regional sub-clusters: bees from Huangshanqu, Qimenxian, Huizhouqu, and Shexian, and those from Yixian. Significant differences between the means of the three clusters were demonstrated using Wilks’ lambda statistic. Morphocluster separation was related to altitude differences. Moreover, we noted some regions with high intercolonial variance, suggesting introgression among these defined honeybee populations.

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Richette, Pascal, Marijn Vis, Sarah Ohrndorf, William Tillett, Julio Ramírez, Marlies Neuhold, Michel van Speybroeck, et al. "Identification of PsA phenotypes with machine learning analytics using data from two phase III clinical trials of guselkumab in a bio-naïve population of patients with PsA." RMD Open 9, no. 1 (March 2023): e002934. http://dx.doi.org/10.1136/rmdopen-2022-002934.

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ObjectivesPsoriatic arthritis (PsA) phenotypes are typically defined by their clinical components, which may not reflect patients’ overlapping symptoms. This post hoc analysis aimed to identify hypothesis-free PsA phenotype clusters using machine learning to analyse data from the phase III DISCOVER-1/DISCOVER-2 clinical trials.MethodsPooled data from bio-naïve patients with active PsA receiving guselkumab 100 mg every 8/4 weeks were retrospectively analysed. Non-negative matrix factorisation was applied as an unsupervised machine learning technique to identify PsA phenotype clusters; baseline patient characteristics and clinical observations were input features. Minimal disease activity (MDA), disease activity index for psoriatic arthritis (DAPSA) low disease activity (LDA) and DAPSA remission at weeks 24 and 52 were evaluated.ResultsEight clusters (n=661) were identified: cluster 1 (feet dominant), cluster 2 (male, overweight, psoriasis dominant), cluster 3 (hand dominant), cluster 4 (dactylitis dominant), cluster 5 (enthesitis, large joints), cluster 6 (enthesitis, small joints), cluster 7 (axial dominant) and cluster 8 (female, obese, large joints). At week 24, MDA response was highest in cluster 2 and lowest in clusters 3, 5 and 6; at week 52, it was highest in cluster 2 and lowest in cluster 5. At weeks 24 and 52, DAPSA LDA and remission were highest in cluster 2 and lowest in clusters 4 and 6, respectively. All clusters improved with guselkumab treatment over 52 weeks.ConclusionsUnsupervised machine learning identified eight PsA phenotype clusters with significant differences in demographics, clinical features and treatment responses. In the future, such data could help support individualised treatment decisions.

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Makogonenko, Evgeny, Olav Andersen, Irina Mikhailenko, Natalya Ananyeva, Alexey Khrenov, Midori Shima, Dudley Strickland, Evgueni Saenko, and Andrey Sarafanov. "Localization of the low-density lipoprotein receptor-related protein regions involved in binding to the A2 domain of coagulation factor VIII." Thrombosis and Haemostasis 98, no. 12 (2007): 1170–81. http://dx.doi.org/10.1160/th07-05-0353.

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SummaryCatabolism of coagulation factorVIII (FVIII) is mediated by lowdensity lipoprotein receptor-related protein (LRP). The ligandbinding sites of LRP are formed by complement-type repeats (CR), and CR clusters II and IV bind most of LRP ligands. FVIII contains two major LRP-binding sites located in the A2 and A3 domains. This study was aimed to identify specific complementtype repeats of LRP involved in interaction with the A2 site and to probe their functional importance in A2 catabolism. We generated individual LRP clusters II, III and IV, along with nine overlapping CR triplets encompassing clusters II and IV in a baculovirus expression system and studied their interaction with isolated A2. In surface plasmon resonance (SPR) assay, A2 bound to clusters II and IV with KDs 22 and 39 nM, respectively, and to the majority of CR triplets with affinities in the range of KDs 25–90 nM. Similar affinities were determined for A2 interaction with a panel of CR doublets overlapping cluster II (CR 3–4, 4–5, 5–6 6–7 and 7–8). These LRP fragments inhibited the binding of 125I-A2 to LRP in solid-phase assay,LRP-mediated internalization of 125I-A2 in cell culture and 125I-A2 clearance from the mouse circulation. Point mutations of critical A2 residues of the LRPbinding site resulted in differential reduction or abolishment of its binding to LRP fragments. We conclude that A2 interacts with LRP via multiple binding sites spanning CR 3–8 in cluster II and CR 23–29 in cluster IV, and the minimal A2-binding unit of LRP is formed by two adjacent CR.

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Aisina, Dana, Raigul Niyazova, Shara Atambayeva, and Anatoliy Ivashchenko. "Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer subtypes." PeerJ 7 (November 13, 2019): e8049. http://dx.doi.org/10.7717/peerj.8049.

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The development of breast cancer (BC) subtypes is controlled by distinct sets of candidate genes, and the expression of these genes is regulated by the binding of their mRNAs with miRNAs. Predicting miRNA associations and target genes is thus essential when studying breast cancer. The MirTarget program identifies the initiation of miRNA binding to mRNA, the localization of miRNA binding sites in mRNA regions, and the free energy from the binding of all miRNA nucleotides with mRNA. Candidate gene mRNAs have clusters (miRNA binding sites with overlapping nucleotide sequences). mRNAs of EPOR, MAZ and NISCH candidate genes of the HER2 subtype have clusters, and there are four clusters in mRNAs of MAZ, BRCA2 and CDK6 genes. Candidate genes of the triple-negative subtype are targets for multiple miRNAs. There are 11 sites in CBL mRNA, five sites in MMP2 mRNA, and RAB5A mRNA contains two clusters in each of the three sites. In SFN mRNA, there are two clusters in three sites, and one cluster in 21 sites. Candidate genes of luminal A and B subtypes are targets for miRNAs: there are 21 sites in FOXA1 mRNA and 15 sites in HMGA2 mRNA. There are clusters of five sites in mRNAs of ITGB1 and SOX4 genes. Clusters of eight sites and 10 sites are identified in mRNAs of SMAD3 and TGFB1 genes, respectively. Organizing miRNA binding sites into clusters reduces the proportion of nucleotide binding sites in mRNAs. This overlapping of miRNA binding sites creates a competition among miRNAs for a binding site. From 6,272 miRNAs studied, only 29 miRNAs from miRBase and 88 novel miRNAs had binding sites in clusters of target gene mRNA in breast cancer. We propose using associations of miRNAs and their target genes as markers in breast cancer subtype diagnosis.

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Laskhmaiah, K., S. Murali Krishna, and B. Eswara Reddy. "An Optimized K-means with Density and Distance-Based Clustering Algorithm for Multidimensional Spatial Databases." International Journal of Computer Network and Information Security 13, no. 6 (December 8, 2021): 70–82. http://dx.doi.org/10.5815/ijcnis.2021.06.06.

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From massive and complex spatial database, the useful information and knowledge are extracted using spatial data mining. To analyze the complexity, efficient clustering algorithm for spatial database has been used in this area of research. The geographic areas containing spatial points are discovered using clustering methods in many applications. With spatial attributes, the spatial clustering problem have been designed using many approaches, but non-overlapping constraints are not considered. Most existing data mining algorithms suffer in high dimensions. With non-overlapping named as Non Overlapping Constraint based Optimized K-Means with Density and Distance-based Clustering (NOC-OKMDDC),a multidimensional optimization clustering is designed to solve this problem by the proposed system and the clusters with diverse shapes and densities in spatial databases are fast found. Proposed method consists of three main phases. Using weighted convolutional Neural Networks(Weighted CNN), attributes are reduced from the multidimensional dataset in this first phase. A partition-based algorithm (K-means) used by Optimized K-Means with Density and Distance-based Clustering (OKMDD) and several relatively small spherical or ball-shaped sub clusters are made by Clustering the dataset in this second phase. The optimal sub cluster count is performed with the help of Adaptive Adjustment Factor based Glowworm Swarm Optimization algorithm (AAFGSO). Then the proposed system designed an Enhanced Penalized Spatial Distance (EPSD) Measure to satisfy the non-overlapping condition. According to the spatial attribute values, the spatial distance between two points are well adjusted to achieving the EPSD. In third phase, to merge sub clusters the proposed system utilizes the Density based clustering with relative distance scheme. In terms of adjusted rand index, rand index, mirkins index and huberts index, better performance is achieved by proposed system when compared to the existing system which is shown by experimental result.

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Fan, Jiande, Weixin Xie, and Haocui Du. "A Robust Multi-Sensor Data Fusion Clustering Algorithm Based on Density Peaks." Sensors 20, no. 1 (December 31, 2019): 238. http://dx.doi.org/10.3390/s20010238.

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In this paper, a novel multi-sensor clustering algorithm, based on the density peaks clustering (DPC) algorithm, is proposed to address the multi-sensor data fusion (MSDF) problem. The MSDF problem is raised in the multi-sensor target detection (MSTD) context and corresponds to clustering observations of multiple sensors, without prior information on clutter. During the clustering process, the data points from the same sensor cannot be grouped into the same cluster, which is called the cannot link (CL) constraint; the size of each cluster should be within a certain range; and overlapping clusters (if any) must be divided into multiple clusters to satisfy the CL constraint. The simulation results confirm the validity and reliability of the proposed algorithm.

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Ouchicha, C., O. Ammor, and M. Meknassi. "A New Validity Index in Overlapping Clusters for Medical Images." Automatic Control and Computer Sciences 54, no. 3 (May 2020): 238–48. http://dx.doi.org/10.3103/s0146411620030050.

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Wang, Rui-Yan, Zhen-Yu Shi, Jin-Chun Chen, and Guo-Qiang Chen. "Cloning Large Gene Clusters fromE. coliUsingin VitroSingle-Strand Overlapping Annealing." ACS Synthetic Biology 1, no. 7 (June 11, 2012): 291–95. http://dx.doi.org/10.1021/sb300025d.

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Vats, Divyanshu, and José M. F. Moura. "Finding Non-Overlapping Clusters for Generalized Inference Over Graphical Models." IEEE Transactions on Signal Processing 60, no. 12 (December 2012): 6368–81. http://dx.doi.org/10.1109/tsp.2012.2214216.

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Zhang, Hongli, Yang Gao, and Yue Zhang. "Overlapping communities from dense disjoint and high total degree clusters." Physica A: Statistical Mechanics and its Applications 496 (April 2018): 286–98. http://dx.doi.org/10.1016/j.physa.2017.12.146.

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Scripps, Jerry, and Pang-Ning Tan. "Constrained overlapping clusters: minimizing the negative effects of bridge-nodes." Statistical Analysis and Data Mining: The ASA Data Science Journal 3, no. 1 (December 30, 2009): 20–37. http://dx.doi.org/10.1002/sam.10066.

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Himmelfarb, Sarah Talia, Nell Bond, Adaora Okoli, John Schieffelin, Jeffrey Shaffer, Robert J. Samuels, and Emily J. Engel. "31. Post-ebola Syndrome Presents with Multiple Overlapping Symptom Clusters: Evidence from an Ongoing Cohort Study in Eastern Sierra Leone." Open Forum Infectious Diseases 7, Supplement_1 (October 1, 2020): S16—S17. http://dx.doi.org/10.1093/ofid/ofaa417.030.

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Abstract Background Since the outbreak of Ebola Virus Disease (EVD) in West Africa from 2013–2016, a large cohort of survivors with persistent health complaints has emerged. This constellation of issues is termed post-Ebola syndrome. Here we characterize the symptoms and physical exam findings of this syndrome in a cohort of survivors from Sierra Leone 2.6 years after resolution of disease. Ebola survivors present with clusters of symptoms that represent sub phenotypes of post-Ebola syndrome Methods Potential survivor participants in Eastern Sierra Leone were identified and recruited through the Sierra Leone Association of Ebola Survivors. Household contacts of survivors were identified by enrolled survivors. Both groups were administered a questionnaire assessing self-reported symptoms. A physical exam was performed by a limited number of trained providers. Symptoms were then compared using hierarchical clustering. Statistical analysis of the correlations between clusters was conducted using conditional logistic regression. Both SPICE and principal component (PCA) analyses were performed to explore the relationships between symptom clusters. Results Between March 2016 and January 2019, 375 Ebola survivors and 1040 contacts were enrolled. At enrollment, Ebola survivors of all age groups reported significantly more symptoms than their contacts in all categories. Six symptom clusters were identified representing distinct organ systems. SPICE revealed 2 general phenotypes: with or without rheumatologic symptoms. Clusters including rheumatologic symptoms were correlated with one another (r = 0.63) but not with other clusters (r < 0.35). Ophthalmologic/auditory symptoms were moderately correlated with the non-rheumatologic clusters (r > 0.5). Interestingly, psychologic/neurologic, cardiac/GI and constitutional clusters correlated with one another (r > 0.6) p < 0.0001 in all cases. The symptom clusters were then mapped onto a PCA. Each symptom cluster separated from the remainder along PC1, particularly the phenotypes with rheumatologic symptoms. Conclusion This study presents an in-depth characterization of post-Ebola syndrome in Sierra Leonean survivors. The interrelationship between symptom clusters indicates that post-Ebola syndrome is a heterogeneous disease. The phenotypes identified may have unique mechanisms of pathogenesis, and require distinct therapies. Disclosures John Schieffelin, MD, MSPH, Wolters-Kluwer (Independent Contractor)

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Bettaieb, Afef, Nabila Filali, Taoufik Filali, and Habib Ben Aissia. "An efficient algorithm for overlapping bubbles segmentation." Computer Optics 44, no. 3 (June 2020): 363–74. http://dx.doi.org/10.18287/2412-6179-co-605.

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Image processing is an effective method for characterizing various two-phase gas/liquid flow systems. However, bubbly flows at a high void fraction impose significant challenges such as diverse bubble shapes and sizes, large overlapping bubble clusters occurrence, as well as out-of-focus bubbles. This study describes an efficient multi-level image processing algorithm for highly overlapping bubbles recognition. The proposed approach performs mainly in three steps: overlapping bubbles classification, contour segmentation and arcs grouping for bubble reconstruction. In the first step, we classify bubbles in the image into a solitary bubble and overlapping bubbles. The purpose of the second step is overlapping bubbles segmentation. This step is performed in two subsequent steps: at first, we classify bubble clusters into touching and communicating bubbles. Then, the boundaries of communicating bubbles are split into segments based on concave point extraction. The last step in our algorithm addresses segments grouping to merge all contour segments that belong to the same bubble and circle/ellipse fitting to reconstruct the missing part of each bubble. An application of the proposed technique to computer generated and high-speed real air bubble images is used to assess our algorithm. The developed method provides an accurate and computationally effective way for overlapping bubbles segmentation. The accuracy rate of well segmented bubbles we achieved is greater than 90 % in all cases. Moreover, a computation time equal to 12 seconds for a typical image (1 Mpx, 150 overlapping bubbles) is reached.

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Castlebury, Frank D., Mark J. Hilsenroth, Leonard Handler, and Thomas W. Durham. "Use of the MMPI-2 Personality Disorder Scales in the Assessment of DSM-IV Antisocial, Borderline, and Narcissistic Personality Disorders." Assessment 4, no. 2 (June 1997): 155–68. http://dx.doi.org/10.1177/107319119700400205.

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This study explored the diagnostic utility of the MMPI-2 Personality Disorder (MMPI-2 PD) scales to correctly classify three Cluster B Personality Disorders (Antisocial, Borderline, and Narcissistic Personality Disorder). Classification was compared against the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) chart diagnoses checked for interrater agreement. MMPI-2 PD scale scores for 53 outpatients diagnosed with a Cluster B Personality Disorder were contrasted with an Other Personality Disorder group ( n = 20) and a nonclinical population ( n = 67). Scores for both the overlapping and nonoverlapping scales of the MMPI-2 PD scales were used in calculating diagnostic efficiency statistics. In support of past findings, results suggest the MMPI-2 PD scales should be used conservatively; they are best at screening for presence or absence of a personality disorder, identifying members of personality disorder clusters, and identifying negative occurrences of specific personality disorders or personality disorder clusters. Findings endorse the use of both versions of the Antisocial Personality Disorder scale and the overlapping version of the Borderline Personality Disorder scale. Use of the Narcissistic Personality Disorder scales is recommended for negative predictive power values only. A multimodal approach is recommended, whereby assessment measures may be used conjointly to improve diagnostic efficiency.

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Jeong, Hoyeon, Yoonbee Kim, Yi-Sue Jung, Dae Ryong Kang, and Young-Rae Cho. "Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins." Entropy 23, no. 10 (September 28, 2021): 1271. http://dx.doi.org/10.3390/e23101271.

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Functional modules can be predicted using genome-wide protein–protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering accuracy, the clustering results are compared to protein complexes and Gene Ontology (GO) annotations as references. We demonstrate that the GE algorithm is more accurate in overlapping clusters than the other competitive methods. Moreover, we confirm the biological feasibility of the proteins that occur most frequently in the set of identified clusters. Finally, novel proteins for the additional annotation of GO terms are revealed.

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