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Academic literature on the topic 'Post-clustering'

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Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Post-clustering"

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Im, Yeong-Hui. "A Post Web Document Clustering Algorithm." KIPS Transactions:PartB 9B, no. 1 (February 1, 2002): 7–16. http://dx.doi.org/10.3745/kipstb.2002.9b.1.007.

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Ong, S. H., and X. Zhao. "On post-clustering evaluation and modification." Pattern Recognition Letters 21, no. 5 (May 2000): 365–73. http://dx.doi.org/10.1016/s0167-8655(00)00003-9.

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Goyal, Poonam, N. Mehala, Divyansh Bhatia, and Navneet Goyal. "Topical document clustering: two-stage post processing technique." International Journal of Data Mining, Modelling and Management 10, no. 2 (2018): 127. http://dx.doi.org/10.1504/ijdmmm.2018.092536.

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Bhatia, Divyansh, Navneet Goyal, Poonam Goyal, and N. Mehala. "Topical document clustering: two-stage post processing technique." International Journal of Data Mining, Modelling and Management 10, no. 2 (2018): 127. http://dx.doi.org/10.1504/ijdmmm.2018.10013658.

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S, Shanmugapriyaa, Kuppusamy K.S, and Aghila G. "Blosen: Blog Search Engine Based on Post Concept Clustering." International Journal of Ambient Systems and Applications 1, no. 3 (September 30, 2013): 17–28. http://dx.doi.org/10.5121/ijasa.2013.1302.

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Lee, Ki-Jun, Kyung-Min Kim, Myung-Jin Lee, Woo-Ju Kim, and June-S. Hong. "Post Clustering Method using Tag Hierarchy for Blog Search." Journal of Society for e-Business Studies 16, no. 4 (November 30, 2011): 301–19. http://dx.doi.org/10.7838/jsebs.2011.16.4.301.

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Liu, Suyuan, Siwei Wang, Pei Zhang, Kai Xu, Xinwang Liu, Changwang Zhang, and Feng Gao. "Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7576–84. http://dx.doi.org/10.1609/aaai.v36i7.20723.

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Multi-view subspace clustering (MVSC) optimally integrates multiple graph structure information to improve clustering performance. Recently, many anchor-based variants are proposed to reduce the computational complexity of MVSC. Though achieving considerable acceleration, we observe that most of them adopt fixed anchor points separating from the subsequential anchor graph construction, which may adversely affect the clustering performance. In addition, post-processing is required to generate discrete clustering labels with additional time consumption. To address these issues, we propose a scalable and parameter-free MVSC method to directly output the clustering labels with optimal anchor graph, termed as Efficient One-pass Multi-view Subspace Clustering with Consensus Anchors (EOMSC-CA). Specially, we combine anchor learning and graph construction into a uniform framework to boost clustering performance. Meanwhile, by imposing a graph connectivity constraint, our algorithm directly outputs the clustering labels without any post-processing procedures as previous methods do. Our proposed EOMSC-CA is proven to be linear complexity respecting to the data size. The superiority of our EOMSC-CA over the effectiveness and efficiency is demonstrated by extensive experiments. Our code is publicly available at https://github.com/Tracesource/EOMSC-CA.
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DiCaprio, Christopher J., Mark Simons, Shelley J. Kenner, and Charles A. Williams. "Post-seismic reloading and temporal clustering on a single fault." Geophysical Journal International 172, no. 2 (February 2008): 581–92. http://dx.doi.org/10.1111/j.1365-246x.2007.03622.x.

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Balabaeva, Ksenia, and Sergey Kovalchuk. "Post-hoc Interpretation of Clinical Pathways Clustering using Bayesian Inference." Procedia Computer Science 178 (2020): 264–73. http://dx.doi.org/10.1016/j.procs.2020.11.028.

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Niemi, Arto, and Jukka Teuhola. "Burrows‐Wheeler post‐transformation with effective clustering and interpolative coding." Software: Practice and Experience 50, no. 9 (June 29, 2020): 1858–74. http://dx.doi.org/10.1002/spe.2873.

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Dissertations / Theses on the topic "Post-clustering"

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Lombardini, Alessandro. "Estrazione di Correlazioni Medicali da Social Post non Etichettati con Language Model Neurali e Data Clustering." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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La progressiva informatizzazione della società a cui il mondo contemporaneo sta assistendo, ha generato un radicale cambiamento nelle abitudini delle persone, le quali oggi giorno trascorrono sempre più tempo online e creano reti di conoscenza prima inimmaginabili. Tale cambiamento ha coinvolto, nel suo avanzare, anche gli individui affetti da malattie di varia natura. In particolare, la scarsa disponibilità di informazioni che caratterizza alcuni contesti medici, unita al bisogno di dialogare con altre persone aventi la medesima problematica, ha determinato negli ultimi anni una forte crescita di comunità sulle piattaforme social, all’interno delle quali vengono scambiati dettagli rispetto a trattamenti, centri specializzati e dottori. In questo senso, i social network sono diventati il luogo in cui i pazienti sono più propensi a condividere le proprie esperienze e opinioni maturate durante il corso della propria malattia. Questa tesi nasce dalla consapevolezza del valore di tali dati e dalla volontà di consentire un ragionamento logico deduttivo al di sopra di essi. Nello specifico, si intende estrarre — con un approccio non supervisionato, mediante l’uso di language model neurali e data clustering — le correlazioni semantiche racchiuse nell’elevata quantità di testo generato dagli utenti attraverso interazioni social, prendendo l’Acalasia Esofagea come caso di studio.
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Idoudi, Rihab. "Fouille de connaissances en diagnostic mammographique par ontologie et règles d'association." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0005/document.

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Face à la complexité significative du domaine mammographique ainsi que l'évolution massive de ses données, le besoin de contextualiser les connaissances au sein d'une modélisation formelle et exhaustive devient de plus en plus impératif pour les experts. C'est dans ce cadre que s'inscrivent nos travaux de recherche qui s'intéressent à unifier différentes sources de connaissances liées au domaine au sein d'une modélisation ontologique cible. D'une part, plusieurs modélisations ontologiques mammographiques ont été proposées dans la littérature, où chaque ressource présente une perspective distincte du domaine d'intérêt. D'autre part, l'implémentation des systèmes d'acquisition des mammographies rend disponible un grand volume d'informations issues des faits passés, dont la réutilisation devient un enjeu majeur. Toutefois, ces fragments de connaissances, présentant de différentes évidences utiles à la compréhension de domaine, ne sont pas interopérables et nécessitent des méthodologies de gestion de connaissances afin de les unifier. C'est dans ce cadre que se situe notre travail de thèse qui s'intéresse à l'enrichissement d'une ontologie de domaine existante à travers l'extraction et la gestion de nouvelles connaissances (concepts et relations) provenant de deux courants scientifiques à savoir: des ressources ontologiques et des bases de données comportant des expériences passées. Notre approche présente un processus de couplage entre l'enrichissement conceptuel et l'enrichissement relationnel d'une ontologie mammographique existante. Le premier volet comporte trois étapes. La première étape dite de pré-alignement d'ontologies consiste à construire pour chaque ontologie en entrée une hiérarchie des clusters conceptuels flous. Le but étant de réduire l'étape d'alignement de deux ontologies entières en un alignement de deux groupements de concepts de tailles réduits. La deuxième étape consiste à aligner les deux structures des clusters relatives aux ontologies cible et source. Les alignements validés permettent d'enrichir l'ontologie de référence par de nouveaux concepts permettant d'augmenter le niveau de granularité de la base de connaissances. Le deuxième processus s'intéresse à l'enrichissement relationnel de l'ontologie mammographique cible par des relations déduites de la base de données de domaine. Cette dernière comporte des données textuelles des mammographies recueillies dans les services de radiologies. Ce volet comporte ces étapes : i) Le prétraitement des données textuelles ii) l'application de techniques relatives à la fouille de données (ou extraction de connaissances) afin d'extraire des expériences de nouvelles associations sous la forme de règles, iii) Le post-traitement des règles générées. Cette dernière consiste à filtrer et classer les règles afin de faciliter leur interprétation et validation par l'expert vi) L'enrichissement de l'ontologie par de nouvelles associations entre les concepts. Cette approche a été mise en 'uvre et validée sur des ontologies mammographiques réelles et des données des patients fournies par les hôpitaux Taher Sfar et Ben Arous
Facing the significant complexity of the mammography area and the massive changes in its data, the need to contextualize knowledge in a formal and comprehensive modeling is becoming increasingly urgent for experts. It is within this framework that our thesis work focuses on unifying different sources of knowledge related to the domain within a target ontological modeling. On the one hand, there is, nowadays, several mammographic ontological modeling, where each resource has a distinct perspective area of interest. On the other hand, the implementation of mammography acquisition systems makes available a large volume of information providing a decisive competitive knowledge. However, these fragments of knowledge are not interoperable and they require knowledge management methodologies for being comprehensive. In this context, we are interested on the enrichment of an existing domain ontology through the extraction and the management of new knowledge (concepts and relations) derived from two scientific currents: ontological resources and databases holding with past experiences. Our approach integrates two knowledge mining levels: The first module is the conceptual target mammographic ontology enrichment with new concepts extracting from source ontologies. This step includes three main stages: First, the stage of pre-alignment. The latter consists on building for each input ontology a hierarchy of fuzzy conceptual clusters. The goal is to reduce the alignment task from two full ontologies to two reduced conceptual clusters. The second stage consists on aligning the two hierarchical structures of both source and target ontologies. Thirdly, the validated alignments are used to enrich the reference ontology with new concepts in order to increase the granularity of the knowledge base. The second level of management is interested in the target mammographic ontology relational enrichment by novel relations deducted from domain database. The latter includes medical records of mammograms collected from radiology services. This section includes four main steps: i) the preprocessing of textual data ii) the application of techniques for data mining (or knowledge extraction) to extract new associations from past experience in the form of rules, iii) the post-processing of the generated rules. The latter is to filter and classify the rules in order to facilitate their interpretation and validation by expert, vi) The enrichment of the ontology by new associations between concepts. This approach has been implemented and validated on real mammographic ontologies and patient data provided by Taher Sfar and Ben Arous hospitals. The research work presented in this manuscript relates to knowledge using and merging from heterogeneous sources in order to improve the knowledge management process
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Ur-Rahman, Nadeem. "Textual data mining applications for industrial knowledge management solutions." Thesis, Loughborough University, 2010. https://dspace.lboro.ac.uk/2134/6373.

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In recent years knowledge has become an important resource to enhance the business and many activities are required to manage these knowledge resources well and help companies to remain competitive within industrial environments. The data available in most industrial setups is complex in nature and multiple different data formats may be generated to track the progress of different projects either related to developing new products or providing better services to the customers. Knowledge Discovery from different databases requires considerable efforts and energies and data mining techniques serve the purpose through handling structured data formats. If however the data is semi-structured or unstructured the combined efforts of data and text mining technologies may be needed to bring fruitful results. This thesis focuses on issues related to discovery of knowledge from semi-structured or unstructured data formats through the applications of textual data mining techniques to automate the classification of textual information into two different categories or classes which can then be used to help manage the knowledge available in multiple data formats. Applications of different data mining techniques to discover valuable information and knowledge from manufacturing or construction industries have been explored as part of a literature review. The application of text mining techniques to handle semi-structured or unstructured data has been discussed in detail. A novel integration of different data and text mining tools has been proposed in the form of a framework in which knowledge discovery and its refinement processes are performed through the application of Clustering and Apriori Association Rule of Mining algorithms. Finally the hypothesis of acquiring better classification accuracies has been detailed through the application of the methodology on case study data available in the form of Post Project Reviews (PPRs) reports. The process of discovering useful knowledge, its interpretation and utilisation has been automated to classify the textual data into two classes.
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Kuoman, Mamani Christian Antonio. "Diversité par clustering pour la recherche d'images : étude expérimentale." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066361/document.

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Les moteurs traditionnels offrent à l'utilisateur des résultats de plus en plus pertinents, mais, dans la plupart des cas, les résultats similaires ont tendance à se regrouper. L'utilisateur peut être intéressé pour retrouver des documents qui soient certes tous pertinents par rapport à sa requête, mais aussi qui soient différents les uns des autres. Dans cette thèse, nous considérons le problème de la diversité pour la recherche d'images. Nous avons focalisé notre attention sur la diversité par l'exploitation du clustering, plus spécialement par une approche hiérarchique (AHC), car sa hiérarchie de clusters peut bien correspondre à la nature hiérarchique de la diversité. De plus, nous proposons une nouvelle approche pour exploiter des ressources plus riches, telle qu'une « arborescence de concepts », pour augmenter la diversité. Différentes approches sont comparées sur trois benchmarks : un qui a été annoté manuellement et qui possède une haute pertinence; et deux publics assez différents et plus généraux. Les résultats montrent que l'exploitation hiérarchique des résultats de l'AHC augmente la diversité en comparaison avec des méthodes de clustering plat standard et avec une méthode de diversité par optimisation. Les résultats montrent aussi l'intérêt d'utiliser une arborescence de concepts comme descripteur pour augmenter la diversité. Enfin, nous avons développé un prototype complet avec la prise en compte des contraintes fortes de temps de calcul ce qui le rend adapté pour être utilisable dans le moteur de recherche de l'entreprise
Conventional search engines return relevant results but often the retrieved items are similar. Moreover, the similar results tend to appear together. The user may be interested to find documents that are relevant and diverse at the same time.In this thesis, we consider the problem of the diversity in image retrieval. We have focused our attention on diversity by clustering, especially in an approach based on an agglomerative hierarchical clustering (AHC) to address the hierarchical nature of the diversity. Furthermore, we propose a novel approach for exploiting richer description resources, such as a «tree of concepts», to increase the diversity.The different approaches are compared on a highly relevant and manually annotated benchmark: the XiloDiv benchmark; and on the most general: ImageClef2008 and MediaEval2013 benchmarks. The experimental results show that a hierarchical exploitation of the results of the AHC increases the diversity in comparison with two flat clustering methods and a method of diversity by optimization. The results also show that it is better to use concept features than visual features from a diversity point of view. In addition, on the Mediaeval2013 benchmark, we show that an interesting strategy to improve diversity is to increase the relevance using the text, and then to exploit visual based clustering to diversify the results.Finally, we developed a complete prototype in particular taking into account the strong constraints of response time which makes it suitable to be used in the company's search engine
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Ribchester, Christopher Brian. "Education policy and the viability of small school provision : the social significance of small primary schools in England and Wales post 1988." Thesis, Aberystwyth University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361003.

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Frauger-Ousset, Elisabeth. "Détournement d'usage de médicaments psychoactifs : développement d'une approche pharmacoépidémiologique." Thesis, Aix-Marseille 2, 2010. http://www.theses.fr/2010AIX20667.

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Ce travail présente le développement d’une nouvelle approche pharmacoépidémiologique, reposant sur les bases de données de l'assurance maladie, permettant de caractériser et d’estimer le détournement d’usage de médicaments psychoactifs. Cette approche utilisant la méthode de classification, regroupe, a posteriori, les sujets en différents sous-groupes, menant à l’identification, la caractérisation et la quantification de différents profils de comportement dont le comportement déviant. Nous avons appliqué cette méthode sur plusieurs médicaments. Pour chaque médicament, nous avons inclus l'ensemble des sujets affiliés au régime général des régions PACA et Corse ayant eu un remboursement. Leurs délivrances ont été suivies sur 9 mois. Après une analyse descriptive, une méthode de classification est appliquée, suivie d’une analyse des différents sous-groupes.Un premier travail a permis de confirmer l'importance du détournement d'usage d'une molécule émergente, le clonazépam (publication n°1). Ensuite nous avons adapté notre méthode afin de pouvoir suivre l'évolution sur plusieurs années de ce détournement (publication n°2). Nous avons appliqué cette méthode pour souligner l’existence, sur plusieurs années, du détournement d'usage du méthylphénidate (publication n°3). Notre équipe avait également développé une autre méthode pour estimer la polyprescription (publication n°4). Enfin, nous avons appliqué de façon conjointe ces deux méthodes (publication n°5). La méthode de classification est de plus en plus utilisée afin de surveiller l'évolution du détournement d'usage de médicaments et commencent à être intégrés au système français de surveillance de l’abus de médicament.aux cotés des autres outils pharmacoépidémiologiques plus traditionnels (OSIAP, OPPIDUM, OPEMA, ASOS, DRAMES)
This work presents the development of a new pharmacoepidemiologic method. This methodallows to estimate abuse of psychoactive prescription drugs in real life using prescriptiondatabase. The method is based on a cluster analysis which is a statistical method used todetermine, a posteriori, different subgroups of subjects. According the subgroups’characteristics, we can determine and estimate different behaviours whose subjects with adeviant behaviour. It assesses the rate of subjects with a deviant behaviour among all thesubjects that obtain the drug from a pharmacy.We used this method on several prescription drugs. For each prescription drug, we includedall individuals, affiliated to the French health reimbursement system of two southern Franceareas (Provence-Alpes-Côte-d’Azur and Corsica), who have had a prescription drugreimbursed during the first weeks of the year. Their deliveries have been monitored over a 9month-period. After a descriptive analysis, a clustering method has been used. The fourquantitative variables used to establish profiles of consumers were : number of differentprescribers, number of different pharmacies, number of dispensings and quantity dispensed(DDD). Finally, the characteristics of different subgroups have been presented, especiallythose with a deviant behavior.The first study using this method allows to confirm and assess the magnitude of the abuseliability of an emerging prescription drug as clonazepam (publication n°1). Then we adapt thismethod in order to follow the abuse evolution during several years. In the second publicationon clonazepam, we identified that the proportion of deviant subjects has increased between2001 and 2006 (from 0.86% to 1.38%). We also applied this method to estimatemethylphenidate abuse during several years (from 2005 to 2008) (publication n°3).Methylphenidate abuse is already describe in other countries whereas few data are available inFrance. This study estimates the proportion of subjects with a deviant behaviour (from 0.5%9in 2005 and in 2006 to 2.0% in 2007 and 1.2% in 2008) and assesses its evolution since theapplication of a specific regulation.Our research team has also developed an other method using prescription database : thedoctor shopping indicator which measures the quantity obtained by doctor shopping amongthe overall quantity reimbursed (publication n°4). The objective of the last publication is toanalyze and compare results from those two methods applied to High Dosage Buprenorphine,a product well-known to be diverted in France.Actually, clustering method is more and more used on prescription drugs in order to assesstheir abuse. The results obtained by this method begin to be included in the other postmarketing surveillance of CNS drugs (OSIAP, OPPIDUM, OPEAM, ASOS, DRAMES)which are used by French public health authorities
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Pons, Nicolas. "Réseau de régulation de l'expression des gènes : Détection de motifs et intégration de données génomiques et post-génomiques : Application chez les streptocoques." Paris 11, 2007. http://www.theses.fr/2007PA112062.

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La modélisation des réseaux de régulation transcriptionnelle des gènes des microorganismes pourrait permettre d’anticiper leur comportement face à des changements environnementaux. Nous proposons de réaliser la structure topologique des réseaux par la caractérisation des sites de fixation des facteurs de transcription. Ceux-ci sont recherchés par des approches bioinformatiques en combinant analyse globale de la transcription (approche intragénomique) et analyse des génomes (approche intergénomique). L’approche intragénomique est basée sur l’exploitation des données du transcriptome en comparant les séquences en amont de gènes corégulés. L’approche intergénomique repose sur l’hypothèse de conservation des schémas de régulation chez des bactéries proches phylogénétiquement, en comparant les séquences en amont de gènes orthologues. Cette dernière approche nécessite d’avoir à disposition une classification des gènes orthologues. Dans cette optique, nous avons développé l’algorithme Scissors optimisé pour écarter au maximum les gènes paralogues biaisant l’approche intergénomique. Les résultats d’orthologie ont été validés par simulation de banques de génomes synthétiques et par un travail d’expertise biologique. De façon à faciliter l’intégration des deux approches, nous avons développé la plateforme iMOMi composée d’une base de données relationnelle et d’un ensemble d’outils dédiés à la détection des sites de fixation. Elle a été utilisée dans l’étude expérimentale de la régulation de plusieurs métabolismes chez Lactococcus lactis, les Streptocoques et d’autres Firmicutes. Elle a permis, en autres, de caractériser les sites de fixation des régulateurs CodY, FruR et FhuR
Modelling the transcriptional regulatory networks allow to set insights in the adaptation mechanisms of living organisms to environmental changes. Here, we propose to build the topological structure of gene expression regulatory networks through the characterization of DNA binding sites. The motif detection is based on bioinformatic approaches combining transcription global analyses (intragenomic approach) and genome comparisons (intergenomic approach). The intragenomic approach consists of comparing upstream sequences of coregulated genes according to transcriptomic data. On the other intergenomic approach, the upstream sequences of orthologous genes are compared. This lies on the expectation that regulatory schemes are conserved between orthologous genes of phylogenetically close bacteria. To build up the orthologous classification, we have developed an original algorithm called Scissors. The algorithm has been optimized to exclude paralogous genes. Scissors has been validated on synthetic genome banks as well as by biological expertise. In order to facilitate the integration of these two approaches, we developed the plateform called iMOMi composed of relational database and a set of software dedicated to the detection of regulatory motifs. The plateform has been used in the experimental studiefds of regulation of several metabolisms in Lactococcus lactis IL1403 as well as Streptococcaea and Firmicutes. The biological relevance of iMOMi has been validated by the DNA binding site characterization of CodY, FruR and FhuR regulators
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Vaerenbergh, Steven Van. "Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals." Doctoral thesis, Universidad de Cantabria, 2010. http://hdl.handle.net/10803/10673.

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En la última década, los métodos kernel (métodos núcleo) han demostrado ser técnicas muy eficaces en la resolución de problemas no lineales. Parte de su éxito puede atribuirse a su sólida base matemática dentro de los espacios de Hilbert generados por funciones kernel ("reproducing kernel Hilbert spaces", RKHS); y al hecho de que resultan en problemas convexos de optimización. Además, son aproximadores universales y la complejidad computacional que requieren es moderada. Gracias a estas características, los métodos kernel constituyen una alternativa atractiva a las técnicas tradicionales no lineales, como las series de Volterra, los polinómios y las redes neuronales. Los métodos kernel también presentan ciertos inconvenientes que deben ser abordados adecuadamente en las distintas aplicaciones, por ejemplo, las dificultades asociadas al manejo de grandes conjuntos de datos y los problemas de sobreajuste ocasionados al trabajar en espacios de dimensionalidad infinita.En este trabajo se desarrolla un conjunto de algoritmos basados en métodos kernel para resolver una serie de problemas no lineales, dentro del ámbito del procesado de señal y las comunicaciones. En particular, se tratan problemas de identificación e igualación de sistemas no lineales, y problemas de separación ciega de fuentes no lineal ("blind source separation", BSS). Esta tesis se divide en tres partes. La primera parte consiste en un estudio de la literatura sobre los métodos kernel. En la segunda parte, se proponen una serie de técnicas nuevas basadas en regresión con kernels para resolver problemas de identificación e igualación de sistemas de Wiener y de Hammerstein, en casos supervisados y ciegos. Como contribución adicional se estudia el campo del filtrado adaptativo mediante kernels y se proponen dos algoritmos recursivos de mínimos cuadrados mediante kernels ("kernel recursive least-squares", KRLS). En la tercera parte se tratan problemas de decodificación ciega en que las fuentes son dispersas, como es el caso en comunicaciones digitales. La dispersidad de las fuentes se refleja en que las muestras observadas se agrupan, lo cual ha permitido diseñar técnicas de decodificación basadas en agrupamiento espectral. Las técnicas propuestas se han aplicado al problema de la decodificación ciega de canales MIMO rápidamente variantes en el tiempo, y a la separación ciega de fuentes post no lineal.
In the last decade, kernel methods have become established techniques to perform nonlinear signal processing. Thanks to their foundation in the solid mathematical framework of reproducing kernel Hilbert spaces (RKHS), kernel methods yield convex optimization problems. In addition, they are universal nonlinear approximators and require only moderate computational complexity. These properties make them an attractive alternative to traditional nonlinear techniques such as Volterra series, polynomial filters and neural networks.This work aims to study the application of kernel methods to resolve nonlinear problems in signal processing and communications. Specifically, the problems treated in this thesis consist of the identification and equalization of nonlinear systems, both in supervised and blind scenarios, kernel adaptive filtering and nonlinear blind source separation.In a first contribution, a framework for identification and equalization of nonlinear Wiener and Hammerstein systems is designed, based on kernel canonical correlation analysis (KCCA). As a result of this study, various other related techniques are proposed, including two kernel recursive least squares (KRLS) algorithms with fixed memory size, and a KCCA-based blind equalization technique for Wiener systems that uses oversampling. The second part of this thesis treats two nonlinear blind decoding problems of sparse data, posed under conditions that do not permit the application of traditional clustering techniques. For these problems, which include the blind decoding of fast time-varying MIMO channels, a set of algorithms based on spectral clustering is designed. The effectiveness of the proposed techniques is demonstrated through various simulations.
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Lee, Ickjai Lee. "Multi-Purpose Boundary-Based Clustering on Proximity Graphs for Geographical Data Mining." Thesis, 2002. http://hdl.handle.net/1959.13/25012.

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With the growth of geo-referenced data and the sophistication and complexity of spatial databases, data mining and knowledge discovery techniques become essential tools for successful analysis of large spatial datasets. Spatial clustering is fundamental and central to geographical data mining. It partitions a dataset into smaller homogeneous groups due to spatial proximity. Resulting groups represent geographically interesting patterns of concentrations for which further investigations should be undertaken to find possible causal factors. In this thesis, we propose a spatial-dominant generalization approach that mines multivariate causal associations among geographical data layers using clustering analysis. First, we propose a generic framework of multi-purpose exploratory spatial clustering in the form of the Template-Method Pattern. Based on an object-oriented framework, we design and implement an automatic multi-purpose exploratory spatial clustering tool. The first instance of this framework uses the Delaunay diagram as an underlying proximity graph. Our spatial clustering incorporates the peculiar characteristics of spatial data that make space special. Thus, our method is able to identify high-quality spatial clusters including clusters of arbitrary shapes, clusters of heterogeneous densities, clusters of different sizes, closely located high-density clusters, clusters connected by multiple chains, sparse clusters near to high-density clusters and clusters containing clusters within O(n log n) time. It derives values for parameters from data and thus maximizes user-friendliness. Therefore, our approach minimizes user-oriented bias and constraints that hinder exploratory data analysis and geographical data mining. Sheer volume of spatial data stored in spatial databases is not the only concern. The heterogeneity of datasets is a common issue in data-rich environments, but left open by exploratory tools. Our spatial clustering extends to the Minkowski metric in the absence or presence of obstacles to deal with situations where interactions between spatial objects are not adequately modeled by the Euclidean distance. The genericity is such that our clustering methodology extends to various spatial proximity graphs beyond the default Delaunay diagram. We also investigate an extension of our clustering to higher-dimensional datasets that robustly identify higher-dimensional clusters within O(n log n) time. The versatility of our clustering is further illustrated with its deployment to multi-level clustering. We develop a multi-level clustering method that reveals hierarchical structures hidden in complex datasets within O(n log n) time. We also introduce weighted dendrograms to effectively visualize the cluster hierarchies. Interpretability and usability of clustering results are of great importance. We propose an automatic pattern spotter that reveals high level description of clusters. We develop an effective and efficient cluster polygonization process towards mining causal associations. It automatically approximates shapes of clusters and robustly reveals asymmetric causal associations among data layers. Since it does not require domain-specific concept hierarchies, its applicability is enhanced.
PhD Doctorate
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Lee, Ickjai Lee. "Multi-Purpose Boundary-Based Clustering on Proximity Graphs for Geographical Data Mining." 2002. http://hdl.handle.net/1959.13/25012.

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With the growth of geo-referenced data and the sophistication and complexity of spatial databases, data mining and knowledge discovery techniques become essential tools for successful analysis of large spatial datasets. Spatial clustering is fundamental and central to geographical data mining. It partitions a dataset into smaller homogeneous groups due to spatial proximity. Resulting groups represent geographically interesting patterns of concentrations for which further investigations should be undertaken to find possible causal factors. In this thesis, we propose a spatial-dominant generalization approach that mines multivariate causal associations among geographical data layers using clustering analysis. First, we propose a generic framework of multi-purpose exploratory spatial clustering in the form of the Template-Method Pattern. Based on an object-oriented framework, we design and implement an automatic multi-purpose exploratory spatial clustering tool. The first instance of this framework uses the Delaunay diagram as an underlying proximity graph. Our spatial clustering incorporates the peculiar characteristics of spatial data that make space special. Thus, our method is able to identify high-quality spatial clusters including clusters of arbitrary shapes, clusters of heterogeneous densities, clusters of different sizes, closely located high-density clusters, clusters connected by multiple chains, sparse clusters near to high-density clusters and clusters containing clusters within O(n log n) time. It derives values for parameters from data and thus maximizes user-friendliness. Therefore, our approach minimizes user-oriented bias and constraints that hinder exploratory data analysis and geographical data mining. Sheer volume of spatial data stored in spatial databases is not the only concern. The heterogeneity of datasets is a common issue in data-rich environments, but left open by exploratory tools. Our spatial clustering extends to the Minkowski metric in the absence or presence of obstacles to deal with situations where interactions between spatial objects are not adequately modeled by the Euclidean distance. The genericity is such that our clustering methodology extends to various spatial proximity graphs beyond the default Delaunay diagram. We also investigate an extension of our clustering to higher-dimensional datasets that robustly identify higher-dimensional clusters within O(n log n) time. The versatility of our clustering is further illustrated with its deployment to multi-level clustering. We develop a multi-level clustering method that reveals hierarchical structures hidden in complex datasets within O(n log n) time. We also introduce weighted dendrograms to effectively visualize the cluster hierarchies. Interpretability and usability of clustering results are of great importance. We propose an automatic pattern spotter that reveals high level description of clusters. We develop an effective and efficient cluster polygonization process towards mining causal associations. It automatically approximates shapes of clusters and robustly reveals asymmetric causal associations among data layers. Since it does not require domain-specific concept hierarchies, its applicability is enhanced.
PhD Doctorate
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Books on the topic "Post-clustering"

1

Schwartz, Carolyn Susan. A study of the application of post-retrieval clustering in bibliographic databases. Ann Arbor, Mich: University Microfilms International, 1986.

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A, Ohnishi, ed. Proceedings of the International Symposium on Clustering Aspects of Quantum Many-Body Systems: Post-symposium of YKIS01, Kyoto, Japan, 12-14 November 2001. River Edge, N.J: World Scientific, 2002.

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Zhiming, Bao. The Systemic Nature of Substratum Transfer. Edited by Markku Filppula, Juhani Klemola, and Devyani Sharma. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199777716.013.024.

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This chapter discusses four grammatical systems in Singapore English that are transferred from Chinese: aspect, pragmatic particles, topicalization, and quantification. Proper analysis of the relevant substrate features reveals extensive clustering: features which form a grammatical system transfer together. Substratum transfer targets the grammatical system, and the transferred system is then exponenced with suitable morphosyntactic materials from the lexifier, filtering out those component features for which the lexifier has no well-formed morphosyntactic exponent. The analysis of the four systems shows that post-transfer stabilization is subject to the normative effect of English. It is argued that data obtained through introspection and corpora are complementary, and substrate-induced grammatical change is best accounted for in a usage-based model that uses the two types of data.
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Book chapters on the topic "Post-clustering"

1

Nghiem, Nguyen-Viet-Dung, Christel Vrain, Thi-Bich-Hanh Dao, and Ian Davidson. "Constrained Clustering via Post-processing." In Discovery Science, 53–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61527-7_4.

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Wolfram, Dietmar. "Clustering for Post Hoc Information Retrieval." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_950-2.

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Wolfram, Dietmar. "Clustering for Post Hoc Information Retrieval." In Encyclopedia of Database Systems, 375–78. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_950.

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Wolfram, Dietmar. "Clustering for Post Hoc Information Retrieval." In Encyclopedia of Database Systems, 484–88. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_950.

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Im, Younghee, Jiyoung Song, and Daihee Park. "Fuzzy Post-clustering Algorithm for Web Search Engine." In Information Retrieval Technology, 709–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11562382_72.

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Inoue, Masashi, and Piyush Grover. "Query Types and Visual Concept-Based Post-retrieval Clustering." In Lecture Notes in Computer Science, 661–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04447-2_83.

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Giancarlo, Raffaele, Giosué Lo Bosco, Luca Pinello, and Filippo Utro. "The Three Steps of Clustering In The Post-Genomic Era." In Biological Knowledge Discovery Handbook, 519–56. Hoboken, New Jersey: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118617151.ch22.

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Jothi, R., Sraban Kumar Mohanty, and Aparajita Ojha. "On the Impact of Post-clustering Phase in Multi-way Spectral Partitioning." In Mining Intelligence and Knowledge Exploration, 161–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26832-3_16.

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Chen, Yimeng, Yan Dong, Emmanuelle Marquis, Zhijie Jiao, Justin Hesterberg, Gary Was, and Peter Chou. "Solute Clustering in As-irradiated and Post-irradiation-Annealed 304 Stainless Steel." In The Minerals, Metals & Materials Series, 2189–207. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-030-04639-2_147.

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Chen, Yimeng, Yan Dong, Emmanuelle Marquis, Zhijie Jiao, Justin Hesterberg, Gary Was, and Peter Chou. "Solute Clustering in As-irradiated and Post-irradiation-Annealed 304 Stainless Steel." In The Minerals, Metals & Materials Series, 973–91. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68454-3_71.

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Conference papers on the topic "Post-clustering"

1

Das, Joydip, Steven J. E. Wilton, Philip Leong, and Wayne Luk. "Modeling post-techmapping and post-clustering FPGA circuit depth." In 2009 International Conference on Field Programmable Logic and Applications (FPL). IEEE, 2009. http://dx.doi.org/10.1109/fpl.2009.5272315.

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Pham, Duc-Son, Ognjen Arandjelovic, and Svetha Venkatesh. "Achieving stable subspace clustering by post-processing generic clustering results." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727496.

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Baronti, Flavio, Alessandro Passaro, and Antonina Starita. "Post-processing clustering to reduce XCS variability." In the 2005 workshops. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1102256.1102272.

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Kumara, Banage T. G. S., Incheon Paik, Hiroki Ohashi, Wuhui Chen, and Koswatte R. C. Koswatte. "Context Aware Post-filtering for Web Service Clustering." In 2014 IEEE International Conference on Services Computing (SCC). IEEE, 2014. http://dx.doi.org/10.1109/scc.2014.65.

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"POST-PROCESSING ASSOCIATION RULES WITH CLUSTERING AND OBJECTIVE MEASURES." In 13th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003457500540063.

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Kumar, Sarvesh, S. K. Jain, and R. M. Sharma. "Diversification of web search results using post-retrieval clustering." In 2014 International Conference on Computer and Communication Technology (ICCCT). IEEE, 2014. http://dx.doi.org/10.1109/iccct.2014.7001460.

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Ichihashi, Hidetomo, Katsuhiro Honda, Naho Kuwamoto, and Takao Hattori. "Post-supervised Fuzzy c-Means Classifier with Hard Clustering." In 2007 IEEE Symposium on Computational Intelligence and Data Mining. IEEE, 2007. http://dx.doi.org/10.1109/cidm.2007.368928.

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Askar, Ahmed, and Andreas Zuefle. "Clustering of Adverse Events of Post-Market Approved Drugs." In SSTD '21: 17th International Symposium on Spatial and Temporal Databases. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3469830.3470903.

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Yoskai, Pitipat, and Sukree Sinthupinyo. "Prediction of Post likes Using Clustering and Regression Technique." In 2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2018. http://dx.doi.org/10.1109/ecai.2018.8678984.

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Han, Junwei, Kai Xiong, and Feiping Nie. "Orthogonal and Nonnegative Graph Reconstruction for Large Scale Clustering." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/251.

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Spectral clustering has been widely used due to its simplicity for solving graph clustering problem in recent years. However, it suffers from the high computational cost as data grow in scale, and is limited by the performance of post-processing. To address these two problems simultaneously, in this paper, we propose a novel approach denoted by orthogonal and nonnegative graph reconstruction (ONGR) that scales linearly with the data size. For the relaxation of Normalized Cut, we add nonnegative constraint to the objective. Due to the nonnegativity, ONGR offers interpretability that the final cluster labels can be directly obtained without post-processing. Extensive experiments on clustering tasks demonstrate the effectiveness of the proposed method.
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