Relevant bibliographies by topics / Clustering based on correlation

Academic literature on the topic 'Clustering based on correlation'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Clustering based on correlation"

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Hua, Jialin, Jian Yu, and Miin-Shen Yang. "Star-based learning correlation clustering." Pattern Recognition 116 (August 2021): 107966. http://dx.doi.org/10.1016/j.patcog.2021.107966.

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Pandove, Divya, Rinkle Rani, and Shivani Goel. "Local graph based correlation clustering." Knowledge-Based Systems 138 (December 2017): 155–75. http://dx.doi.org/10.1016/j.knosys.2017.09.034.

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Sato-Ilic, Mika. "On Fuzzy Clustering based Correlation." Procedia Computer Science 12 (2012): 230–35. http://dx.doi.org/10.1016/j.procs.2012.09.061.

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Zhu, Guibo, Jinqiao Wang, and Hanqing Lu. "Clustering based ensemble correlation tracking." Computer Vision and Image Understanding 153 (December 2016): 55–63. http://dx.doi.org/10.1016/j.cviu.2016.05.006.

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Rao .S, Venkata. "Correlation Preserving Indexing Based Text Clustering." IOSR Journal of Computer Engineering 13, no. 1 (2013): 27–30. http://dx.doi.org/10.9790/0661-1312730.

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Pandove, Divya, Shivani Goel, and Rinkle Rani. "General correlation coefficient based agglomerative clustering." Cluster Computing 22, no. 2 (November 2, 2018): 553–83. http://dx.doi.org/10.1007/s10586-018-2863-y.

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Hua, Jia-Lin, Jian Yu, and Miin-Shen Yang. "Correlative Density-Based Clustering." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 6935–43. http://dx.doi.org/10.1166/jctn.2016.5650.

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Mountains, which heap up by densities of a data set, intuitively reflect the structure of data points. These mountain clustering methods are useful for grouping data points. However, the previous mountain-based clustering suffers from the choice of parameters which are used to compute the density. In this paper, we adopt correlation analysis to determine the density, and propose a new clustering algorithm, called Correlative Density-based Clustering (CDC). The new algorithm computes the density with a modified way and determines the parameters based on the inherent structure of data points. Experiments on artificial datasets and real datasets demonstrate the simplicity and effectiveness of the proposed approach.
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Jain, Aaditya, and Suchita Tyagi. "Priority Based New Approach for Correlation Clustering." International Journal of Information Technology and Computer Science 9, no. 3 (March 8, 2017): 71–79. http://dx.doi.org/10.5815/ijitcs.2017.03.08.

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Sudhher, P. "Clustering Algorithm Based On Correlation Preserving Indexing." IOSR Journal of Computer Engineering 15, no. 3 (2013): 58–63. http://dx.doi.org/10.9790/0661-1535863.

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Chiou, Jeng-Min, and Pai-Ling Li. "Correlation-Based Functional Clustering via Subspace Projection." Journal of the American Statistical Association 103, no. 484 (December 2008): 1684–92. http://dx.doi.org/10.1198/016214508000000814.

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Dissertations / Theses on the topic "Clustering based on correlation"

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Rosén, Fredrik. "Correlation based clustering of the Stockholm Stock Exchange." Thesis, Stockholm University, School of Business, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-6500.

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This thesis present a topological classification of stocks traded on the Stockholm Stock Exchange based solely on the co-movements between individual stocks. The working hypothesis is that an ultrametric space is an appropriate space for linking stocks together. The hierarchical structure is obtained from the matrix of correlation coefficient computed between all pairs of stocks included in the OMXS~30 portfolio by considering the daily logarithmic return. The dynamics of the system is investigated by studying the distribution and time dependence of the correlation coefficients. Average linkage clustering is proposed as an alternative to the conventional single linkage clustering. The empirical investigation show that the Minimum-Spanning Tree (the graphical representation of the clustering procedure) describe the reciprocal arrangement of the stocks included in the investigated portfolio in a way that also makes sense from an economical point of view. Average linkage clustering results in five main clusters, consisting of Machinery, Bank, Telecom, Paper & Forest and Security companies. Most groups are homogeneous with respect to their sector and also often with respect to their sub-industry, as specified by the GICS classification standard. E.g. the Bank cluster consists of the Commercial Bank companies FöreningsSparbanken, SEB, Handelsbanken and Nordea. However, there are also examples where companies form cluster without belonging to the same sector. One example of this is the Security cluster, consisting of ASSA (Building Products) and Securitas (Diversified Commercial \& Professional Services). Even if they belong to different industries, both are active in the security area. ASSA is a manufacturer and supplier of locking solutions and SECU focus on guarding solutions, security systems and cash handling. The empirical results show that it is possible to obtain a meaningful taxonomy based solely on the co-movements between individual stocks and the fundamental ultrametric assumption, without any presumptions of the companies business activity. The obtained clusters indicate that common economical factors can affect certain groups of stocks, irrespective of their GICS industry classification. The outcome of the investigation is of fundamental importance for e.g. asset classification and portfolio optimization, where the co-movement between assets is of vital importance.

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Pettersson, Christoffer. "Investigating the Correlation Between Marketing Emails and Receivers Using Unsupervised Machine Learning on Limited Data : A comprehensive study using state of the art methods for text clustering and natural language processing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189147.

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The goal of this project is to investigate any correlation between marketing emails and their receivers using machine learning and only a limited amount of initial data. The data consists of roughly 1200 emails and 98.000 receivers of these. Initially, the emails are grouped together based on their content using text clustering. They contain no information regarding prior labeling or categorization which creates a need for an unsupervised learning approach using solely the raw text based content as data. The project investigates state-of-the-art concepts like bag-of-words for calculating term importance and the gap statistic for determining an optimal number of clusters. The data is vectorized using term frequency - inverse document frequency to determine the importance of terms relative to the document and to all documents combined. An inherit problem of this approach is high dimensionality which is reduced using latent semantic analysis in conjunction with singular value decomposition. Once the resulting clusters have been obtained, the most frequently occurring terms for each cluster are analyzed and compared. Due to the absence of initial labeling an alternative approach is required to evaluate the clusters validity. To do this, the receivers of all emails in each cluster who actively opened an email is collected and investigated. Each receiver have different attributes regarding their purpose of using the service and some personal information. Once gathered and analyzed, conclusions could be drawn that it is possible to find distinguishable connections between the resulting email clusters and their receivers but to a limited extent. The receivers from the same cluster did show similar attributes as each other which were distinguishable from the receivers of other clusters. Hence, the resulting email clusters and their receivers are specific enough to distinguish themselves from each other but too general to handle more detailed information. With more data, this could become a useful tool for determining which users of a service should receive a particular email to increase the conversion rate and thereby reach out to more relevant people based on previous trends.
Målet med detta projekt att undersöka eventuella samband mellan marknadsföringsemail och dess mottagare med hjälp av oövervakad maskininlärning på en brgränsad mängd data. Datan består av ca 1200 email meddelanden med 98.000 mottagare. Initialt så gruperas alla meddelanden baserat på innehåll via text klustering. Meddelandena innehåller ingen information angående tidigare gruppering eller kategorisering vilket skapar ett behov för ett oövervakat tillvägagångssätt för inlärning där enbart det råa textbaserade meddelandet används som indata. Projektet undersöker moderna tekniker så som bag-of-words för att avgöra termers relevans och the gap statistic för att finna ett optimalt antal kluster. Datan vektoriseras med hjälp av term frequency - inverse document frequency för att avgöra relevansen av termer relativt dokumentet samt alla dokument kombinerat. Ett fundamentalt problem som uppstår via detta tillvägagångssätt är hög dimensionalitet, vilket reduceras med latent semantic analysis tillsammans med singular value decomposition. Då alla kluster har erhållits så analyseras de mest förekommande termerna i vardera kluster och jämförs. Eftersom en initial kategorisering av meddelandena saknas så krävs ett alternativt tillvägagångssätt för evaluering av klustrens validitet. För att göra detta så hämtas och analyseras alla mottagare för vardera kluster som öppnat något av dess meddelanden. Mottagarna har olika attribut angående deras syfte med att använda produkten samt personlig information. När de har hämtats och undersökts kan slutsatser dras kring hurvida samband kan hittas. Det finns ett klart samband mellan vardera kluster och dess mottagare, men till viss utsträckning. Mottagarna från samma kluster visade likartade attribut som var urskiljbara gentemot mottagare från andra kluster. Därav kan det sägas att de resulterande klustren samt dess mottagare är specifika nog att urskilja sig från varandra men för generella för att kunna handera mer detaljerad information. Med mer data kan detta bli ett användbart verktyg för att bestämma mottagare av specifika emailutskick för att på sikt kunna öka öppningsfrekvensen och därmed nå ut till mer relevanta mottagare baserat på tidigare resultat.
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Zimek, Arthur. "Correlation Clustering." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-87361.

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To, Thang Long Information Technology &amp Electrical Engineering Australian Defence Force Academy UNSW. "Video object segmentation using phase-base detection of moving object boundaries." Awarded by:University of New South Wales - Australian Defence Force Academy. School of Information Technology and Electrical Engineering, 2005. http://handle.unsw.edu.au/1959.4/38705.

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A video sequence often contains a number of objects. For each object, the motion of its projection on the video frames is affected by its movement in 3-D space, as well as the movement of the camera. Video object segmentation refers to the task of delineating and distinguishing different objects that exist in a series of video frames. Segmentation of moving objects from a two-dimensional video is difficult due to the lack of depth information at the boundaries between different objects. As the motion incoherency of a region is intrinsically linked to the presence of such boundaries and vice versa, a failure to recognise a discontinuity in the motion field, or the use of an incorrect motion, often leads directly to errors in the segmentation result. In addition, many defects in a segmentation mask are also located in the vicinity of moving object boundaries, due to the unreliability of motion estimation in these regions. The approach to segmentation in this work comprises of three stages. In the first part, a phase-based method is devised for detection of moving object boundaries. This detection scheme is based on the characteristics of a phase-matched difference image, and is shown to be sensitive to even small disruptions to a coherent motion field. In the second part, a spatio-temporal approach for object segmentation is introduced, which involves a spatial segmentation in the detected boundary region, followed by a motion-based region-merging operation using three temporally adjacent video frames. In the third stage, a multiple-frame approach for stabilisation of object masks is introduced to alleviate the defects which may have existed earlier in a local segmentation, and to improve upon the temporal consistency of object boundaries in the segmentation masks along a sequence. The feasibility of the proposed work is demonstrated at each stage through examples carried out on a number of real video sequences. In the presence of another object motion, the phase-based boundary detection method is shown to be much more sensitive than direct measures such as sum-of-squared error on a motion-compensated difference image. The three-frame segmentation scheme also compares favourably with a recently proposed method initiated from a non-selective spatial segmentation. In addition, improvements in the quality of the object masks after the stabilisation stage are also observed both quantitatively and visually. The final segmentation result is then used in an experimental object-based video compression framework, which also shows improvements in efficiency over a contemporary video coding method.
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Ren, Jinchang. "Semantic content analysis for effective video segmentation, summarisation and retrieval." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/4251.

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This thesis focuses on four main research themes namely shot boundary detection, fast frame alignment, activity-driven video summarisation, and highlights based video annotation and retrieval. A number of novel algorithms have been proposed to address these issues, which can be highlighted as follows. Firstly, accurate and robust shot boundary detection is achieved through modelling of cuts into sub-categories and appearance based modelling of several gradual transitions, along with some novel features extracted from compressed video. Secondly, fast and robust frame alignment is achieved via the proposed subspace phase correlation (SPC) and an improved sub-pixel strategy. The SPC is proved to be insensitive to zero-mean-noise, and its gradient-based extension is even robust to non-zero-mean noise and can be used to deal with non-overlapped regions for robust image registration. Thirdly, hierarchical modelling of rush videos using formal language techniques is proposed, which can guide the modelling and removal of several kinds of junk frames as well as adaptive clustering of retakes. With an extracted activity level measurement, shot and sub-shot are detected for content-adaptive video summarisation. Fourthly, highlights based video annotation and retrieval is achieved, in which statistical modelling of skin pixel colours, knowledge-based shot detection, and improved determination of camera motion patterns are employed. Within these proposed techniques, one important principle is to integrate various kinds of feature evidence and to incorporate prior knowledge in modelling the given problems. High-level hierarchical representation is extracted from the original linear structure for effective management and content-based retrieval of video data. As most of the work is implemented in the compressed domain, one additional benefit is the achieved high efficiency, which will be useful for many online applications.
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Batet, Sanromà Montserrat. "Ontology based semantic clustering." Doctoral thesis, Universitat Rovira i Virgili, 2011. http://hdl.handle.net/10803/31913.

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Els algoritmes de clustering desenvolupats fins al moment s’han centrat en el processat de dades numèriques i categòriques, no considerant dades textuals. Per manegar adequadament aquestes dades, es necessari interpretar el seu significat a nivell semàntic. En aquest treball es presenta un nou mètode de clustering que es capaç d’interpretar, de forma integrada, dades numèriques, categòriques i textuals. Aquest últims es processaran mitjançant mesures de similitud semàntica basades en 1) la utilització del coneixement taxonòmic contingut en una o diferents ontologies i 2) l’estimació de la distribució de la informació dels termes a la Web. Els resultats mostren que una interpretació precisa de la informació textual a nivell semàntic millora els resultats del clustering i facilita la interpretació de les classificacions.
Clustering algorithms have focused on the management of numerical and categorical data. However, in the last years, textual information has grown in importance. Proper processing of this kind of information within data mining methods requires an interpretation of their meaning at a semantic level. In this work, a clustering method aimed to interpret, in an integrated manner, numerical, categorical and textual data is presented. Textual data will be interpreted by means of semantic similarity measures. These measures calculate the alikeness between words by exploiting one or several knowledge sources. In this work we also propose two new ways of compute semantic similarity based on 1) the exploitation of the taxonomical knowledge available on one or several ontologies and 2) the estimation of the information distribution of terms in the Web. Results show that a proper interpretation of textual data at a semantic level improves clustering results and eases the interpretability of the classifications
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Luo, Yongfeng. "Range-Based Graph Clustering." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1014606422.

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Fuentes, Garcia Ruth S. "Bayesian model-based clustering." Thesis, University of Bath, 2004. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412350.

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Albarakati, Rayan. "Density Based Data Clustering." CSUSB ScholarWorks, 2015. https://scholarworks.lib.csusb.edu/etd/134.

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Data clustering is a data analysis technique that groups data based on a measure of similarity. When data is well clustered the similarities between the objects in the same group are high, while the similarities between objects in different groups are low. The data clustering technique is widely applied in a variety of areas such as bioinformatics, image segmentation and market research. This project conducted an in-depth study on data clustering with focus on density-based clustering methods. The latest density-based (CFSFDP) algorithm is based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively larger distance from points with higher densities. This method has been examined, experimented, and improved. These methods (KNN-based, Gaussian Kernel-based and Iterative Gaussian Kernel-based) are applied in this project to improve (CFSFDP) density-based clustering. The methods are applied to four milestone datasets and the results are analyzed and compared.
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Faria, Rodrigo Augusto Dias. "Human skin segmentation using correlation rules on dynamic color clustering." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-01102018-101814/.

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Human skin is made of a stack of different layers, each of which reflects a portion of impinging light, after absorbing a certain amount of it by the pigments which lie in the layer. The main pigments responsible for skin color origins are melanin and hemoglobin. Skin segmentation plays an important role in a wide range of image processing and computer vision applications. In short, there are three major approaches for skin segmentation: rule-based, machine learning and hybrid. They differ in terms of accuracy and computational efficiency. Generally, machine learning and hybrid approaches outperform the rule-based methods but require a large and representative training dataset and, sometimes, costly classification time as well, which can be a deal breaker for real-time applications. In this work, we propose an improvement, in three distinct versions, of a novel method for rule-based skin segmentation that works in the YCbCr color space. Our motivation is based on the hypotheses that: (1) the original rule can be complemented and, (2) human skin pixels do not appear isolated, i.e. neighborhood operations are taken into consideration. The method is a combination of some correlation rules based on these hypotheses. Such rules evaluate the combinations of chrominance Cb, Cr values to identify the skin pixels depending on the shape and size of dynamically generated skin color clusters. The method is very efficient in terms of computational effort as well as robust in very complex images.
A pele humana é constituída de uma série de camadas distintas, cada uma das quais reflete uma porção de luz incidente, depois de absorver uma certa quantidade dela pelos pigmentos que se encontram na camada. Os principais pigmentos responsáveis pela origem da cor da pele são a melanina e a hemoglobina. A segmentação de pele desempenha um papel importante em uma ampla gama de aplicações em processamento de imagens e visão computacional. Em suma, existem três abordagens principais para segmentação de pele: baseadas em regras, aprendizado de máquina e híbridos. Elas diferem em termos de precisão e eficiência computacional. Geralmente, as abordagens com aprendizado de máquina e as híbridas superam os métodos baseados em regras, mas exigem um conjunto de dados de treinamento grande e representativo e, por vezes, também um tempo de classificação custoso, que pode ser um fator decisivo para aplicações em tempo real. Neste trabalho, propomos uma melhoria, em três versões distintas, de um novo método de segmentação de pele baseado em regras que funciona no espaço de cores YCbCr. Nossa motivação baseia-se nas hipóteses de que: (1) a regra original pode ser complementada e, (2) pixels de pele humana não aparecem isolados, ou seja, as operações de vizinhança são levadas em consideração. O método é uma combinação de algumas regras de correlação baseadas nessas hipóteses. Essas regras avaliam as combinações de valores de crominância Cb, Cr para identificar os pixels de pele, dependendo da forma e tamanho dos agrupamentos de cores de pele gerados dinamicamente. O método é muito eficiente em termos de esforço computacional, bem como robusto em imagens muito complexas.
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Books on the topic "Clustering based on correlation"

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Pedrycz, Witold. Knowledge-Based Clustering. New York: John Wiley & Sons, Ltd., 2005.

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Biehl, Michael, Barbara Hammer, Michel Verleysen, and Thomas Villmann, eds. Similarity-Based Clustering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01805-3.

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Pedrycz, Witold. Knowledge-Based Clustering. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471708607.

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Botbol, Joseph Moses. Multivariate clustering based on entropy. [Washington]: U.S. G.P.O., 1989.

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Botbol, Joseph Moses. Multivariate clustering based on entropy. Washington, DC: Dept. of the Interior, 1989.

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Jordan, James. Correlation-based measurement systems. Hemel Hempstead: Horwood, 1989.

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1949-, Bishop Peter, and Kiani Bijan, eds. Correlation-based measurement systems. Chichester: E. Horwood, 1989.

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Vathy-Fogarassy, Ágnes. Graph-Based Clustering and Data Visualization Algorithms. London: Springer London, 2013.

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Streekmann, Niels. Clustering-Based Support for Software Architecture Restructuring. Wiesbaden: Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8675-0.

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Vathy-Fogarassy, Ágnes, and János Abonyi. Graph-Based Clustering and Data Visualization Algorithms. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5158-6.

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Book chapters on the topic "Clustering based on correlation"

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Rebagliati, Nicola, Samuel Rota Bulò, and Marcello Pelillo. "Correlation Clustering with Stochastic Labellings." In Similarity-Based Pattern Recognition, 120–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39140-8_8.

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Aszalós, László, and Tamás Mihálydeák. "Rough Classification Based on Correlation Clustering." In Rough Sets and Knowledge Technology, 399–410. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11740-9_37.

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Song, Shaoxu, and Chunping Li. "Semantic Correlation Network Based Text Clustering." In AI 2005: Advances in Artificial Intelligence, 604–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11589990_63.

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Alush, Amir, and Jacob Goldberger. "Break and Conquer: Efficient Correlation Clustering for Image Segmentation." In Similarity-Based Pattern Recognition, 134–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39140-8_9.

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Mulay, Preeti, and Kaustubh Shinde. "Personalized Diabetes Analysis Using Correlation-Based Incremental Clustering Algorithm." In Studies in Big Data, 167–93. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0550-4_8.

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Fukunaga, Takuro. "LP-Based Pivoting Algorithm for Higher-Order Correlation Clustering." In Lecture Notes in Computer Science, 51–62. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-94776-1_5.

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Wang, Baijie, and Xin Wang. "Spatial Entropy-Based Clustering for Mining Data with Spatial Correlation." In Advances in Knowledge Discovery and Data Mining, 196–208. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20841-6_17.

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Wang, Ning, and Jie Li. "Restoring: A Greedy Heuristic Approach Based on Neighborhood for Correlation Clustering." In Advanced Data Mining and Applications, 348–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53914-5_30.

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Hazarika, Anil, A. Sarmah, M. Boro, P. Kalita, and B. K. Dev Choudhury. "Discriminant Correlation-Based Information Fusion for Real-Time Biomedical Signal Clustering." In Advances in Communication, Devices and Networking, 465–74. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7901-6_51.

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Chen, Chuan-Liang, Yun-Chao Gong, and Ying-Jie Tian. "KCK-Means: A Clustering Method Based on Kernel Canonical Correlation Analysis." In Computational Science – ICCS 2008, 995–1004. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69384-0_104.

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Conference papers on the topic "Clustering based on correlation"

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Zhenya Zhang, Hongmei Cheng, Wanli Chen, Shuguang Zhang, and Qiansheng Fang. "Correlation clustering based on genetic algorithm for documents clustering." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4631230.

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Zhenya Zhang, Hongmei Cheng, and Shuguang Zhang. "Approach to SOM based correlation clustering." In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4597772.

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Yeo, Myung Ho, Mi Sook Lee, Seok Jae Lee, and Jae Soo Yoo. "Data Correlation-Based Clustering in Sensor Networks." In 2008 International Symposium on Computer Science and its Applications (CSA). IEEE, 2008. http://dx.doi.org/10.1109/csa.2008.21.

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Malhotra, Akshay, Kazi T. Shahid, and Ioannis D. Schizas. "Unsupervised Kernel Learning for Correlation Based Clustering." In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645425.

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Banna, Hasan Ul, Talha Iqbal, Ayesha Khan, and Zoupash Zahra. "Generators coherency identification using relative correlation based clustering." In 2018 International Conference on Engineering and Emerging Technologies (ICEET). IEEE, 2018. http://dx.doi.org/10.1109/iceet1.2018.8338625.

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Shivhare, Anubhav, Manish Kumar Maurya, Vatsal J. Sanglani, and Manish Kumar. "Spatial Correlation Based Device Level Clustering for IoT." In 2019 IEEE Conference on Information and Communication Technology (CICT). IEEE, 2019. http://dx.doi.org/10.1109/cict48419.2019.9066136.

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MICCICHE’, S., F. LILLO, and R. N. MANTEGNA. "CORRELATION BASED HIERARCHICAL CLUSTERING IN FINANCIAL TIME SERIES." In Proceedings of the 31st Workshop of the International School of Solid State Physics. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812701558_0037.

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Yuan, Li, and Shixiong Xia. "A Correlation-Based Clustering Hierarchical P2P Network Model." In 2010 International Conference on Internet Technology and Applications (iTAP). IEEE, 2010. http://dx.doi.org/10.1109/itapp.2010.5566396.

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Tao, Shun, Yongtong Li, Xiangning Xiao, and Liting Yao. "Load forecasting based on short-term correlation clustering." In 2017 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia). IEEE, 2017. http://dx.doi.org/10.1109/isgt-asia.2017.8378416.

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Kou Takahashi, T. Miura, and I. Shioya. "Clustering Web Documents Based on Correlation of Hyperlinks." In 21st International Conference on Data Engineering Workshops (ICDEW'05). IEEE, 2005. http://dx.doi.org/10.1109/icde.2005.204.

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Reports on the topic "Clustering based on correlation"

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Kanani, Pallika, and Andrew McCallum. Resource-Bounded Information Gathering for Correlation Clustering. Fort Belvoir, VA: Defense Technical Information Center, January 2007. http://dx.doi.org/10.21236/ada464769.

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Oh, Man-Suk, and Adrian Raftery. Model-based Clustering with Dissimilarities: A Bayesian Approach. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada459759.

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Merl, D. Advances in Bayesian Model Based Clustering Using Particle Learning. Office of Scientific and Technical Information (OSTI), November 2009. http://dx.doi.org/10.2172/1010386.

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Hulsegge, B., and K. H. de Greef. Clustering of farms based on slaughterhouse health aberration data. Wageningen: Wageningen Livestock Research, 2017. http://dx.doi.org/10.18174/415138.

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Erpam, Mert Kemal. Tweets on a tree: Index-based clustering of tweets. Sabanci University, April 2017. http://dx.doi.org/10.5900/su_fens_wp.2017.31274.

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Fraley, Chris, and Adrian E. Raftery. Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering. Fort Belvoir, VA: Defense Technical Information Center, August 2005. http://dx.doi.org/10.21236/ada454825.

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Fraley, Chris, Adrian Raftery, and Ron Wehrensy. Incremental Model-Based Clustering for Large Datasets With Small Clusters. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada459790.

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Barnes, C. S. Binary decision clustering for neural network based optical character recognition. Gaithersburg, MD: National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5542.

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Wehrens, Ron, Lutgarde M. Buydens, Chris Fraley, and Adrian E. Raftery. Model-Based Clustering for Image Segmentation and Large Datasets Via Sampling. Fort Belvoir, VA: Defense Technical Information Center, February 2003. http://dx.doi.org/10.21236/ada459638.

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Murtagh, Fionn, Adrian E. Raftery, and Jean-Luc Starck. Bayesian Inference for Color Image Quantization via Model-Based Clustering Trees. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada459791.

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