Relevant bibliographies by topics / HYBRID CLUSTERING

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'HYBRID CLUSTERING'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "HYBRID CLUSTERING"

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Osei-Bryson, Kweku-Muata, and Tasha R. Inniss. "A hybrid clustering algorithm." Computers & Operations Research 34, no. 11 (November 2007): 3255–69. http://dx.doi.org/10.1016/j.cor.2005.12.004.

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Ikotun, Abiodun M., and Absalom E. Ezugwu. "Boosting k-means clustering with symbiotic organisms search for automatic clustering problems." PLOS ONE 17, no. 8 (August 11, 2022): e0272861. http://dx.doi.org/10.1371/journal.pone.0272861.

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Kmeans clustering algorithm is an iterative unsupervised learning algorithm that tries to partition the given dataset into k pre-defined distinct non-overlapping clusters where each data point belongs to only one group. However, its performance is affected by its sensitivity to the initial cluster centroids with the possibility of convergence into local optimum and specification of cluster number as the input parameter. Recently, the hybridization of metaheuristics algorithms with the K-Means algorithm has been explored to address these problems and effectively improve the algorithm’s performance. Nonetheless, most metaheuristics algorithms require rigorous parameter tunning to achieve an optimum result. This paper proposes a hybrid clustering method that combines the well-known symbiotic organisms search algorithm with K-Means using the SOS as a global search metaheuristic for generating the optimum initial cluster centroids for the K-Means. The SOS algorithm is more of a parameter-free metaheuristic with excellent search quality that only requires initialising a single control parameter. The performance of the proposed algorithm is investigated by comparing it with the classical SOS, classical K-means and other existing hybrids clustering algorithms on eleven (11) UCI Machine Learning Repository datasets and one artificial dataset. The results from the extensive computational experimentation show improved performance of the hybrid SOSK-Means for solving automatic clustering compared to the standard K-Means, symbiotic organisms search clustering methods and other hybrid clustering approaches.
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Augusteijn, M. F., and U. J. Steck. "Supervised adaptive clustering: A hybrid neural network clustering algorithm." Neural Computing & Applications 7, no. 1 (March 1998): 78–89. http://dx.doi.org/10.1007/bf01413712.

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Yu, Zhiwen, Le Li, Yunjun Gao, Jane You, Jiming Liu, Hau-San Wong, and Guoqiang Han. "Hybrid clustering solution selection strategy." Pattern Recognition 47, no. 10 (October 2014): 3362–75. http://dx.doi.org/10.1016/j.patcog.2014.04.005.

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Amiri, Saeid, Bertrand S. Clarke, Jennifer L. Clarke, and Hoyt Koepke. "A General Hybrid Clustering Technique." Journal of Computational and Graphical Statistics 28, no. 3 (March 18, 2019): 540–51. http://dx.doi.org/10.1080/10618600.2018.1546593.

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Javed, Ali, and Byung Suk Lee. "Hybrid semantic clustering of hashtags." Online Social Networks and Media 5 (March 2018): 23–36. http://dx.doi.org/10.1016/j.osnem.2017.10.004.

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Chen, Yan, and Qin Zhou Niu. "Hybrid Clustering Algorithm Based on KNN and MCL." Applied Mechanics and Materials 610 (August 2014): 302–6. http://dx.doi.org/10.4028/www.scientific.net/amm.610.302.

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MCL is a graph clustering algorithm. With the characteristics of the MCL computational process, MCL is prone to producing small clustering and separating edge nodes from the group. A hybrid clustering based on MCL combined with KNN algorithm is proposed. Hybrid algorithm improves the quality of clustering by reclassification of elements in small clustering by using KNN classification characteristics and Clustering tables required by MCL clustering. Experiment proves the improved algorithm can enhance the quality of clustering.
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P. Saveetha, P. Saveetha, Y. Harold Robinson P. Saveetha, Vimal Shanmuganathan Y. Harold Robinson, Seifedine Kadry Vimal Shanmuganathan, and Yunyoung Nam Seifedine Kadry. "Hybrid Energy-based Secured Clustering technique for Wireless Sensor Networks." 網際網路技術學刊 23, no. 1 (January 2022): 021–31. http://dx.doi.org/10.53106/160792642022012301003.

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<p>The performance of the Wireless sensor networks (WSNs) identified as the efficient energy utilization and enhanced network lifetime. The multi-hop path routing techniques in WSNs have been observed that the applications with the data transmission within the cluster head and the base station, so that the intra-cluster transmission has been involved for improving the quality of service. This paper proposes a novel Hybrid Energy-based Secured Clustering (HESC) technique for providing the data transmission technique for WSNs to produce the solution for the energy and security problem for cluster based data transmission. The proposed technique involves the formation of clusters to perform the organization of sensor nodes with the multi-hop data transmission technique for finding the specific node to deliver the data packets to the cluster head node and the secured transmission technique is used to provide the privacy of the sensor nodes through the cluster. The residual energy of the sensor nodes is another parameter to select the forwarding node. The simulation results can show the efficiency of this proposed technique in spite of lifetime within the huge amount data packets. The security of this proposed technique is measured and increases the performance of the proposed technique.</p> <p>&nbsp;</p>
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LIU, YONGGUO, XIAORONG PU, YIDONG SHEN, ZHANG YI, and XIAOFENG LIAO. "CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM." International Journal on Artificial Intelligence Tools 16, no. 06 (December 2007): 919–34. http://dx.doi.org/10.1142/s021821300700362x.

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In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated.
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Yang, Wenlu, Yinghui Zhang, Hongjun Wang, Ping Deng, and Tianrui Li. "Hybrid genetic model for clustering ensemble." Knowledge-Based Systems 231 (November 2021): 107457. http://dx.doi.org/10.1016/j.knosys.2021.107457.

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Dissertations / Theses on the topic "HYBRID CLUSTERING"

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Keller, Jens. "Clustering biological data using a hybrid approach : Composition of clusterings from different features." Thesis, University of Skövde, School of Humanities and Informatics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-1078.

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Clustering of data is a well-researched topic in computer sciences. Many approaches have been designed for different tasks. In biology many of these approaches are hierarchical and the result is usually represented in dendrograms, e.g. phylogenetic trees. However, many non-hierarchical clustering algorithms are also well-established in biology. The approach in this thesis is based on such common algorithms. The algorithm which was implemented as part of this thesis uses a non-hierarchical graph clustering algorithm to compute a hierarchical clustering in a top-down fashion. It performs the graph clustering iteratively, with a previously computed cluster as input set. The innovation is that it focuses on another feature of the data in each step and clusters the data according to this feature. Common hierarchical approaches cluster e.g. in biology, a set of genes according to the similarity of their sequences. The clustering then reflects a partitioning of the genes according to their sequence similarity. The approach introduced in this thesis uses many features of the same objects. These features can be various, in biology for instance similarities of the sequences, of gene expression or of motif occurences in the promoter region. As part of this thesis not only the algorithm itself was implemented and evaluated, but a whole software also providing a graphical user interface. The software was implemented as a framework providing the basic functionality with the algorithm as a plug-in extending the framework. The software is meant to be extended in the future, integrating a set of algorithms and analysis tools related to the process of clustering and analysing data not necessarily related to biology.

The thesis deals with topics in biology, data mining and software engineering and is divided into six chapters. The first chapter gives an introduction to the task and the biological background. It gives an overview of common clustering approaches and explains the differences between them. Chapter two shows the idea behind the new clustering approach and points out differences and similarities between it and common clustering approaches. The third chapter discusses the aspects concerning the software, including the algorithm. It illustrates the architecture and analyses the clustering algorithm. After the implementation the software was evaluated, which is described in the fourth chapter, pointing out observations made due to the use of the new algorithm. Furthermore this chapter discusses differences and similarities to related clustering algorithms and software. The thesis ends with the last two chapters, namely conclusions and suggestions for future work. Readers who are interested in repeating the experiments which were made as part of this thesis can contact the author via e-mail, to get the relevant data for the evaluation, scripts or source code.

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Tyree, Eric William. "A hybrid methodology for data clustering." Thesis, City University London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301057.

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Moore, Garrett Lee. "A Hybrid (Active-Passive) VANET Clustering Technique." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1077.

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Clustering serves a vital role in the operation of Vehicular Ad hoc Networks (VANETs) by continually grouping highly mobile vehicles into logical hierarchical structures. These moving clusters support Intelligent Transport Systems (ITS) applications and message routing by establishing a more stable global topology. Clustering increases scalability of the VANET by eliminating broadcast storms caused by packet flooding and facilitate multi-channel operation. Clustering techniques are partitioned in research into two categories: active and passive. Active techniques rely on periodic beacon messages from all vehicles containing location, velocity, and direction information. However, in areas of high vehicle density, congestion may occur on the long-range channel used for beacon messages limiting the scale of the VANET. Passive techniques use embedded information in the packet headers of existing traffic to perform clustering. In this method, vehicles not transmitting traffic may cause cluster heads to contain stale and malformed clusters. This dissertation presents a hybrid active/passive clustering technique, where the passive technique is used as a congestion control strategy for areas where congestion is detected in the network. In this case, cluster members halt their periodic beacon messages and utilize embedded position information in the header to update the cluster head of their position. This work demonstrated through simulation that the hybrid technique reduced/eliminated the delays caused by congestion in the modified Distributed Coordination Function (DCF) process, thus increasing the scalability of VANETs in urban environments. Packet loss and delays caused by the hidden terminal problem was limited to distant, non-clustered vehicles. This dissertation report presents a literature review, methodology, results, analysis, and conclusion.
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Gurcan, Fatih. "A Hybrid Movie Recommender Using Dynamic Fuzzy Clustering." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611667/index.pdf.

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Recommender systems are information retrieval tools helping users in their information seeking tasks and guiding them in a large space of possible options. Many hybrid recommender systems are proposed so far to overcome shortcomings born of pure content-based (PCB) and pure collaborative filtering (PCF) systems. Most studies on recommender systems aim to improve the accuracy and efficiency of predictions. In this thesis, we propose an online hybrid recommender strategy (CBCFdfc) based on content boosted collaborative filtering algorithm which aims to improve the prediction accuracy and efficiency. CBCFdfc combines content-based and collaborative characteristics to solve problems like sparsity, new item and over-specialization. CBCFdfc uses fuzzy clustering to keep a certain level of prediction accuracy while decreasing online prediction time. We compare CBCFdfc with PCB and PCF according to prediction accuracy metrics, and with CBCFonl (online CBCF without clustering) according to online recommendation time. Test results showed that CBCFdfc performs better than other approaches in most cases. We, also, evaluate the effect of user-specified parameters to the prediction accuracy and efficiency. According to test results, we determine optimal values for these parameters. In addition to experiments made on simulated data, we also perform a user study and evaluate opinions of users about recommended movies. The results that are obtained in user evaluation are satisfactory. As a result, the proposed system can be regarded as an accurate and efficient hybrid online movie recommender.
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Tantrum, Jeremy. "Model based and hybrid clustering of large datasets /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/8933.

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Garbiso, Julian Pedro. "Fair auto-adaptive clustering for hybrid vehicular networks." Thesis, Paris, ENST, 2017. http://www.theses.fr/2017ENST0061/document.

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Dans le cadre du développement des innovations dans les Systèmes de Transport Intelligents, les véhicules connectés devront être capables de télécharger des informations basées sur la position sur et depuis des serveurs distants. Ces véhicules seront équipés avec des différentes technologies d’accès radio, telles que les réseaux cellulaires ou les réseaux véhicule-à-véhicule (V2V) comme IEEE 802.11p. Les réseaux cellulaires, avec une couverture presque omniprésente, fournissent un accès à internet avec garanties de qualité de service. Cependant, l’accès à ces réseaux est payant. Dans cette thèse, un algorithme de clustering multi-saut est proposé avec pour objectif de réduire le coût d’accès au réseau cellulaire en agrégeant des données sur le réseau V2V. Pour faire ceci, le leader du cluster (CH, de l’anglais Cluster Head) est utilisé comme passerelle unique vers le réseau cellulaire. Pour le test d’une application d’exemple pour télécharger du Floating Car Data agrégé, les résultats des simulations montrent que cette approche réduit l’utilisation du réseau cellulaire de plus de 80%, en s’attaquant à la redondance typique des données basées sur la position dans les réseaux véhiculaires. Il y a une contribution en trois parties : Premièrement, une approche pour déléguer la sélection du CH à la station de base du réseau cellulaire afin de maximiser la taille des clusters, et par conséquent le taux de compression. Deuxièmement, un algorithme auto-adaptatif qui change dynamiquement le nombre maximum de sauts afin de maintenir un équilibre entre la réduction des coûts d’accès au réseau cellulaire et le taux de perte de paquets dans le réseau V2V. Finalement, l’incorporation d’une théorie de la justice distributive, afin d’améliorer l’équité sur la durée concernant la distribution des coûts auxquels les CH doivent faire face, améliorant ainsi l’acceptabilité sociale de la proposition. Les algorithmes proposés ont été testés via simulation, et les résultats montrent une réduction significative dans l’utilisation du réseau cellulaire, une adaptation réussie du nombre de sauts aux changements de la densité du trafic véhiculaire, et une amélioration dans les métriques d’équité, sans affecter la performance des réseaux
For the development of innovative Intelligent Transportation Systems applications, connected vehicles will frequently need to upload and download position-based information to and from servers. These vehicles will be equipped with different Radio Access Technologies (RAT), like cellular and vehicle-to-vehicle (V2V) technologies such as LTE and IEEE 802.11p respectively. Cellular networkscan provide internet access almost anywhere, with QoS guarantees. However, accessing these networks has an economic cost. In this thesis, a multi-hop clustering algorithm is proposed in the aim of reducing the cellular access costs by aggregating information and off-loading data in the V2V network, using the Cluster Head as a single gateway to the cellular network. For the example application of uploading aggregated Floating Car Data, simulation results show that this approach reduce cellular data consumption by more than 80% by reducing the typical redundancy of position-based data in a vehicular network. There is a threefold contribution: First, an approach that delegates the Cluster Head selection to the cellular base station in order to maximize the cluster size, thus maximizing aggregation. Secondly, a self-adaptation algorithm that dynamically changes the maximum number of hops, addressing the trade-off between cellular access reduction and V2V packet loss. Finally, the incorporation of a theory of distributive justice, for improving fairness over time regarding the distribution of the cost in which Cluster Heads have to incur, thus improving the proposal’s social acceptability. The proposed algorithms were tested via simulation, and the results show a significant reduction in cellular network usage, a successful adaptation of the number of hops to changes in the vehicular traffic density, and an improvement in fairness metrics, without affecting network performance
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Javed, Ali. "A Hybrid Approach to Semantic Hashtag Clustering in Social Media." ScholarWorks @ UVM, 2016. http://scholarworks.uvm.edu/graddis/623.

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The uncontrolled usage of hashtags in social media makes them vary a lot in the quality of semantics and the frequency of usage. Such variations pose a challenge to the current approaches which capitalize on either the lexical semantics of a hashtag by using metadata or the contextual semantics of a hashtag by using the texts associated with a hashtag. This thesis presents a hybrid approach to clustering hashtags based on their semantics, designed in two phases. The first phase is a sense-level metadata-based semantic clustering algorithm that has the ability to differentiate among distinct senses of a hashtag as opposed to the hashtag word itself. The gold standard test demonstrates that sense-level clusters are significantly more accurate than word-level clusters. The second phase is a hybrid semantic clustering algorithm using a consensus clustering approach which finds the consensus between metadata-based sense-level semantic clusters and text-based semantic clusters. The gold standard test shows that the hybrid algorithm outperforms both the text-based algorithm and the metadata-based algorithm for a majority of ground truths tested and that it never underperforms both baseline algorithms. In addition, a larger-scale performance study, conducted with a focus on disagreements in cluster assignments between algorithms, shows that the hybrid algorithm makes the correct cluster assignment in a majority of disagreement cases.
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GARRAFFA, MICHELE. "Exact and Heuristic Hybrid Approaches for Scheduling and Clustering Problems." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2639115.

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This thesis deals with the design of exact and heuristic algorithms for scheduling and clustering combinatorial optimization problems. All the works are linked by the fact that all the presented methods arebasically hybrid algorithms, that mix techniques used in the world of combinatorial optimization. The algorithms are all efficient in practice, but the one presented in Chapter 4, that has mostly theoretical interest. Chapter 2 presents practical solution algorithms based on an ILP model for an energy scheduling combinatorial problem that arises in a smart building context. Chapter 3 presents a new cutting stock problem and introduce a mathematical formulation and a heuristic solution approach based on a heuristic column generation scheme. Chapter 4 provides an exact exponential algorithm, whose importance is only theoretical so far, for a classical scheduling problem: the Single Machine Total Tardiness Problem. The relevant aspect is that the designed algorithm has the best worst case complexity for the problem, that has been studied for several decades. Furthermore, such result is based on a new technique, called Branch and Merge, that avoids the solution of several equivalent sub-problems in a branching algorithm that requires polynomial space. As a consequence, such technique embeds in a branching algorithm ideas coming from other traditional computer science techniques such as dynamic programming and memorization, but keeping the space requirement polynomial. Chapter 5 provides an exact approach based on semidefinite programming and a matheuristic approach based on a quadratic solver for a fractional clustering combinatorial optimization problem, called Max-Mean Dispersion Problem. The matheuristic approach has the peculiarity of using a non-linear MIP solver. The proposed exact approach uses a general semidefinite programming relaxation and it is likely to be extended to other combinatorial problems with a fractional formulation. Chapter 6 proposes practical solution methods for a real world clustering problem arising in a smart city context. The solution algorithm is based on the solution of a Set Cover model via a commercial ILP solver. As a conclusion, the main contribution of this thesis is given by several approaches of practical or theoretical interest, for two classes of important combinatorial problems: clustering and scheduling. All the practical methods presented in the thesis are validated by extensive computational experiments, that compare the proposed methods with the ones available in the state of the art.
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Masoudi, Pedram. "Application of hybrid uncertainty-clustering approach in pre-processing well-logs." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S023/document.

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La thèse est principalement centrée sur l'étude de la résolution verticale des diagraphies. On outre, l'arithmétique floue est appliquée aux modèles expérimentaux pétrophysiques en vue de transmettre l'incertitude des données d'entrée aux données de sortie, ici la saturation irréductible en eau et la perméabilité. Les diagraphies sont des signaux digitaux dont les données sont des mesures volumétriques. Le mécanisme d'enregistrement de ces données est modélisé par des fonctions d'appartenance floues. On a montré que la Résolution Verticale de la Fonction d'Appartenance (VRmf) est supérieur d'espacement. Dans l'étape suivante, la fréquence de Nyquist est revue en fonction du mécanisme volumétrique de diagraphie ; de ce fait, la fréquence volumétrique de Nyquist est proposée afin d'analyser la précision des diagraphies. Basé sur le modèle de résolution verticale développée, un simulateur géométrique est conçu pour générer les registres synthétiques d'une seule couche mince. Le simulateur nous permet d'analyser la sensibilité des diagraphies en présence d'une couche mince. Les relations de régression entre les registres idéaux (données d'entrée de ce simulateur) et les registres synthétiques (données de sortie de ce simulateur) sont utilisées comme relations de déconvolution en vue d'enlever l'effet des épaules de couche d'une couche mince sur les diagraphies GR, RHOB et NPHI. Les relations de déconvolution ont bien été appliquées aux diagraphies pour caractériser les couches minces. Par exemple, pour caractériser une couche mince poreuse, on a eu recours aux données de carottage qui étaient disponibles pour la vérification : NPHI mesuré (3.8%) a été remplacé (corrigé) par 11.7%. NPHI corrigé semble être plus précis que NPHI mesuré, car la diagraphie a une valeur plus grande que la porosité de carottage (8.4%). Il convient de rappeler que la porosité totale (NPHI) ne doit pas être inférieure à la porosité effective (carottage). En plus, l'épaisseur de la couche mince a été estimée à 13±7.5 cm, compatible avec l'épaisseur de la couche mince dans la boite de carottage (<25 cm). Normalement, l'épaisseur in situ est inférieure à l'épaisseur de la boite de carottage, parce que les carottes obtenues ne sont plus soumises à la pression lithostatique, et s'érodent à la surface du sol. La DST est appliquée aux diagraphies, et l'intervalle d'incertitude de DST est construit. Tandis que la VRmf des diagraphies GR, RHOB, NPHI et DT est ~60 cm, la VRmf de l'intervalle d'incertitude est ~15 cm. Or, on a perdu l'incertitude de la valeur de diagraphie, alors que la VRmf est devenue plus précise. Les diagraphies ont été ensuite corrigées entre l'intervalle d'incertitude de DST avec quatre simulateurs. Les hautes fréquences sont amplifiées dans les diagraphies corrigées, et l'effet des épaules de couche est réduit. La méthode proposée est vérifiée dans les cas synthétiques, la boite de carottage et la porosité de carotte. L'analyse de partitionnement est appliquée aux diagraphies NPHI, RHOB et DT en vue de trouver l'intervalle d'incertitude, basé sur les grappes. Puis, le NPHI est calibré par la porosité de carottes dans chaque grappe. Le √MSE de NPHI calibré est plus bas par rapport aux cinq modèles conventionnels d'estimation de la porosité (au minimum 33% d'amélioration du √MSE). Le √MSE de généralisation de la méthode proposée entre les puits voisins est augmenté de 42%. L'intervalle d'incertitude de la porosité est exprimé par les nombres flous. L'arithmétique floue est ensuite appliquée dans le but de calculer les nombres flous de la saturation irréductible en eau et de la perméabilité. Le nombre flou de la saturation irréductible en eau apporte de meilleurs résultats en termes de moindre sous-estimation par rapport à l'estimation nette. Il est constaté que lorsque les intervalles de grappes de porosité ne sont pas compatibles avec la porosité de carotte, les nombres flous de la perméabilité ne sont pas valables
In the subsurface geology, characterization of geological beds by well-logs is an uncertain task. The thesis mainly concerns studying vertical resolution of well-logs (question 1). In the second stage, fuzzy arithmetic is applied to experimental petrophysical relations to project the uncertainty range of the inputs to the outputs, here irreducible water saturation and permeability (question 2). Regarding the first question, the logging mechanism is modelled by fuzzy membership functions. Vertical resolution of membership function (VRmf) is larger than spacing and sampling rate. Due to volumetric mechanism of logging, volumetric Nyquist frequency is proposed. Developing a geometric simulator for generating synthetic-logs of a single thin-bed enabled us analysing sensitivity of the well-logs to the presence of a thin-bed. Regression-based relations between ideal-logs (simulator inputs) and synthetic-logs (simulator outputs) are used as deconvolution relations for removing shoulder-bed effect of thin-beds from GR, RHOB and NPHI well-logs. NPHI deconvolution relation is applied to a real case where the core porosity of a thin-bed is 8.4%. The NPHI well-log is 3.8%, and the deconvolved NPHI is 11.7%. Since it is not reasonable that the core porosity (effective porosity) be higher than the NPHI (total porosity), the deconvolved NPHI is more accurate than the NPHI well-log. It reveals that the shoulder-bed effect is reduced in this case. The thickness of the same thin-bed was also estimated to be 13±7.5 cm, which is compatible with the thickness of the thin-bed in the core box (<25 cm). Usually, in situ thickness is less than the thickness of the core boxes, since at the earth surface, there is no overburden pressure, also the cores are weathered. Dempster-Shafer Theory (DST) was used to create well-log uncertainty range. While the VRmf of the well-logs is more than 60 cm, the VRmf of the belief and plausibility functions (boundaries of the uncertainty range) would be about 15 cm. So, the VRmf is improved, while the certainty of the well-log value is lost. In comparison with geometric method, DST-based algorithm resulted in a smaller uncertainty range of GR, RHOB and NPHI logs by 100%, 71% and 66%, respectively. In the next step, cluster analysis is applied to NPHI, RHOB and DT for the purpose of providing cluster-based uncertainty range. Then, NPHI is calibrated by core porosity value in each cluster, showing low √MSE compared to the five conventional porosity estimation models (at least 33% of improvement in √MSE). Then, fuzzy arithmetic is applied to calculate fuzzy numbers of irreducible water saturation and permeability. Fuzzy number of irreducible water saturation provides better (less overestimation) results than the crisp estimation. It is found that when the cluster interval of porosity is not compatible with the core porosity, the permeability fuzzy numbers are not valid, e.g. in well#4. Finally, in the possibilistic approach (the fuzzy theory), by calibrating α-cut, the right uncertainty interval could be achieved, concerning the scale of the study
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Hung, Chih-Li. "An adaptive SOM model for document clustering using hybrid neural techniques." Thesis, University of Sunderland, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.400460.

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Books on the topic "HYBRID CLUSTERING"

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De, Sourav, Sandip Dey, and Siddhartha Bhattacharyya, eds. Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley, 2020. http://dx.doi.org/10.1002/9781119551621.

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Bhattacharyya, Siddhartha, Sourav De, and Sandip Dey. Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley & Sons, Limited, John, 2020.

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Bhattacharyya, Siddhartha, Sourav De, and Sandip Dey. Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley & Sons, Incorporated, John, 2020.

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Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley & Sons, Limited, John, 2020.

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Bhattacharyya, Siddhartha, Sourav De, and Sandip Dey. Recent Advances in Hybrid Metaheuristics for Data Clustering. Wiley & Sons, Incorporated, John, 2020.

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Kaur, Arvind, and Nancy Nancy. Comparative Analysis of Hybrid Clustering Algorithm with K- Means. Independently Published, 2018.

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Book chapters on the topic "HYBRID CLUSTERING"

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Awange, Joseph, Béla Paláncz, and Lajos Völgyesi. "Clustering." In Hybrid Imaging and Visualization, 149–95. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26153-5_3.

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Przybyła, Tomasz. "Hybrid Fuzzy Clustering Method." In Advances in Soft Computing, 60–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75175-5_8.

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Kogan, Jacob, Charles Nicholas, and Mike Wiacek. "Hybrid Clustering with Divergences." In Survey of Text Mining II, 65–85. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-046-9_4.

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Singh, Tribhuvan, Krishn Kumar Mishra, and Ranvijay. "Data Clustering Using Environmental Adaptation Method." In Hybrid Intelligent Systems, 156–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49336-3_16.

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Oliveira, A. C. M., and L. A. N. Lorena. "Hybrid Evolutionary Algorithms and Clustering Search." In Hybrid Evolutionary Algorithms, 77–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_4.

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Panwar, Surya Nandan, Saliya Goyal, and Prafulla Bafna. "Analytical Study of Starbucks Using Clustering." In Hybrid Intelligent Systems, 1013–21. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27409-1_93.

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Chong, A., T. D. Gedeon, and K. W. Wong. "Histogram-Based Fuzzy Clustering and Its Comparison to Fuzzy C-Means Clustering in One-Dimensional Data." In Hybrid Information Systems, 253–67. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1782-9_19.

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Sarkar, Jnanendra Prasad, Indrajit Saha, Anasua Sarkar, and Ujjwal Maulik. "Improving Modified Differential Evolution for Fuzzy Clustering." In Hybrid Intelligent Systems, 136–46. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76351-4_14.

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Smith, Kate A., Sheldon Chuan, and Peter Putten. "Determining the Validity of Clustering for Data Fusion." In Hybrid Information Systems, 627–36. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1782-9_45.

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Bhoi, Swati G., and Ujwala M. Patil. "Hybrid Clustering Based Smart Crawler." In Communications in Computer and Information Science, 137–44. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1423-0_16.

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Conference papers on the topic "HYBRID CLUSTERING"

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Chandra, B. "Hybrid clustering algorithm." In 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2009. http://dx.doi.org/10.1109/icsmc.2009.5346251.

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Srinoy, Surat, and Werasak Kurutach. "Combination Artificial Ant Clustering and K-PSO Clustering Approach to Network Security Model." In 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253601.

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Jiang, Sheng-Yi, and Xia Li. "A Hybrid Clustering Algorithm." In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2009. http://dx.doi.org/10.1109/fskd.2009.93.

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Niennattrakul, V., and C. A. Ratanamahatana. "Clustering Multimedia Data Using Time Series." In 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253514.

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Hwang, Junwon, Dooheon Song, and Changhoon Lee. "Performance Analysis of 2-tier Clustering." In 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253659.

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Faceli, Katti, Andre De Carvalho, and Marcilio De Souto. "Multi-Objective Clustering Ensemble." In 2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06). IEEE, 2006. http://dx.doi.org/10.1109/his.2006.264934.

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Yoo, Jungsoon Park, Chrisila C. Pettey, and Sung Yoo. "A hybrid conceptual clustering system." In the 1996 ACM 24th annual conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/228329.228341.

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Oduntan, Olayinka Idowu, and Parimala Thulasiraman. "Hybrid Metaheuristic Algorithm for Clustering." In 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2018. http://dx.doi.org/10.1109/ssci.2018.8628863.

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Dehshibi, Mohammad Mahdi, Meysam Alavi, and Jamshid Shanbehzadeh. "Kernel-based Persian viseme clustering." In 2013 13th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2013. http://dx.doi.org/10.1109/his.2013.6920468.

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Chimphlee, W., A. H. Abdullah, M. Noor Md Sap, S. Srinoy, and S. Chimphlee. "Anomaly-Based Intrusion Detection using Fuzzy Rough Clustering." In 2006 International Conference on Hybrid Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/ichit.2006.253508.

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Reports on the topic "HYBRID CLUSTERING"

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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Abstract:
The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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