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Relevant bibliographies by topics / Overlapping Clusters

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Academic literature on the topic 'Overlapping Clusters'

Author: Grafiati

Published: 8 September 2023

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Journal articles on the topic "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 densi

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2

Singh, Sukhminder. "Estimation in overlapping clusters." Communications in Statistics - Theory and Methods 17, no. 2 (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 (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

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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 (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 t

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Qing, Huan. "Studying Asymmetric Structure in Directed Networks by Overlapping and Non-Overlapping Models." Entropy 24, no. 9 (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 ex

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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 (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 eval

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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

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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 (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

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9

Lee, Kyung-Soon. "Resampling Feedback Documents Using Overlapping Clusters." KIPS Transactions:PartB 16B, no. 3 (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 (1985): 231–32. http://dx.doi.org/10.1177/0008068319850312.

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