Relevant bibliographies by topics / Tree data

Contents

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

Academic literature on the topic 'Tree data'

Author: Grafiati
Published: 28 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Tree data"

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D, Christy Sujatha, and Gnana Jayanthi Dr.J. "LASH Tree: LASSO Regression Hoeffding for Streaming Data." International Journal of Psychosocial Rehabilitation 24, no. 04 (February 28, 2020): 3022–33. http://dx.doi.org/10.37200/ijpr/v24i4/pr201415.

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Dobashi, Nao, Shota Saito, Yuta Nakahara, and Toshiyasu Matsushima. "Meta-Tree Random Forest: Probabilistic Data-Generative Model and Bayes Optimal Prediction." Entropy 23, no. 6 (June 18, 2021): 768. http://dx.doi.org/10.3390/e23060768.

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This paper deals with a prediction problem of a new targeting variable corresponding to a new explanatory variable given a training dataset. To predict the targeting variable, we consider a model tree, which is used to represent a conditional probabilistic structure of a targeting variable given an explanatory variable, and discuss statistical optimality for prediction based on the Bayes decision theory. The optimal prediction based on the Bayes decision theory is given by weighting all the model trees in the model tree candidate set, where the model tree candidate set is a set of model trees in which the true model tree is assumed to be included. Because the number of all the model trees in the model tree candidate set increases exponentially according to the maximum depth of model trees, the computational complexity of weighting them increases exponentially according to the maximum depth of model trees. To solve this issue, we introduce a notion of meta-tree and propose an algorithm called MTRF (Meta-Tree Random Forest) by using multiple meta-trees. Theoretical and experimental analyses of the MTRF show the superiority of the MTRF to previous decision tree-based algorithms.
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Randall, Bryan L., Alan R. Ek, Jerold T. Hahn, and Roland G. Buchman. "STEMS Model Projection Capability with Incomplete Tree List Input Data." Northern Journal of Applied Forestry 5, no. 3 (September 1, 1988): 190–94. http://dx.doi.org/10.1093/njaf/5.3.190.

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Abstract Projections were made using the STEMS individual tree based stand growth model for plots in red pine, maple-birch, and aspen cover types for periods up to 50 years. Effects of incomplete tree list input data on plots in the form of small tree censorship (omission of small trees) and tree list aggregation (by size class) were examined by comparing projections made for complete plot tree lists (controls) with projections made after these tree lists were censored and aggregated (treatments). Basal area and number of trees estimates proved highly sensitive to censorship, while volume estimates were much less sensitive. Augmentation of censored distributions by an “average” small tree distribution for the cover type resulted in significant improvement of these estimates. Projection model capability using input data aggregated by size class depended on the degree of aggregation. For some types of aggregation, for example by 2-in. dbh classes, the STEMS model retains much of its predictive utility. North. J. Appl. For. 5:190-194, Sept. 1988.
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Hsiao, Pei-Yung. "Nearly Balanced Quad List Quad Tree -A Data Structure for VLSI Layout Systems." VLSI Design 4, no. 1 (January 1, 1996): 17–32. http://dx.doi.org/10.1155/1996/82789.

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In the past ten years, many researchers have focused attention on developing better data structures for storing graphical information. Among the proposed data structures, the quad tree data structure provides a good way to organize objects on a 2-D plane. Region searches proceed at logarithmic speeds a desirable characteristic, but no previously proposed VLSI quad tree data structure distributed objects to subdivide the spatial area. This has been a major drawback for operations such as tree searching and window query. In this paper, we present a new division method to reconstruct those quad trees including the multiple storage quad tree (MSQT) and the quad list quad tree (QLQT) into nearly balanced quad tree data structures. Nearly balanced quad trees based on our new spatial division method are constructed by dynamically translating unbalanced multiple storage quad trees or unbalanced quad list quad trees into balanced structures. All benefits of the original quad tree data structures are completely retained. In addition, this method is simple and balanced quad trees memory require less than the original quad trees. Experimental results illustrate that the improvement in region queries of the presented nearly balanced quad trees to both of the QLQT and the MSQT is better than the improvement of the QLQT to the MSQT.
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Tennekes, Martijn, and Edwin de Jonge. "Tree Colors: Color Schemes for Tree-Structured Data." IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (December 31, 2014): 2072–81. http://dx.doi.org/10.1109/tvcg.2014.2346277.

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Guan, H., S. Cao, Y. Yu, J. Li, N. Liu, P. Chen, and Y. Li. "STREET-SCENE TREE SEGMENTATION FROM MOBILE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 221–25. http://dx.doi.org/10.5194/isprsarchives-xli-b3-221-2016.

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Our work addresses the problem of extracting trees from mobile laser scanning data. The work is a two step-wise strategy, including terrain point removal and tree segmentation. First, a voxel-based upward growing filtering is proposed to remove terrain points from the mobile laser scanning data. Then, a tree segmentation is presented to extract individual trees via a Euclidean distance clustering approach and Voxel-based Normalized Cut (VNCut) segmentation approach. A road section data acquired by a RIEGL VMX-450 system are selected for evaluating the proposed tree segmentation method. Qualitative analysis shows that our algorithm achieves a good performance.
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Guan, H., S. Cao, Y. Yu, J. Li, N. Liu, P. Chen, and Y. Li. "STREET-SCENE TREE SEGMENTATION FROM MOBILE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 221–25. http://dx.doi.org/10.5194/isprs-archives-xli-b3-221-2016.

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Our work addresses the problem of extracting trees from mobile laser scanning data. The work is a two step-wise strategy, including terrain point removal and tree segmentation. First, a voxel-based upward growing filtering is proposed to remove terrain points from the mobile laser scanning data. Then, a tree segmentation is presented to extract individual trees via a Euclidean distance clustering approach and Voxel-based Normalized Cut (VNCut) segmentation approach. A road section data acquired by a RIEGL VMX-450 system are selected for evaluating the proposed tree segmentation method. Qualitative analysis shows that our algorithm achieves a good performance.
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Woodard, P. M., D. Needham, W. E. Phillips, and L. F. Constantino. "A Christmas tree market analysis: implications from Alberta, Canada data." Forestry Chronicle 70, no. 4 (August 1, 1994): 443–48. http://dx.doi.org/10.5558/tfc70443-4.

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The 1990 Christmas tree market in Alberta, Canada was assessed in an attempt to determine the feasibility of growing such trees for local consumption. Almost 5 500 questionnaires were delivered to households and commercial establishments as part of this survey. In addition, many personal interviews were conducted. The information presented pertains to the wholesale and retail sales volumes and values by tree species. The socio-economic background of tree buying consumers and their traditional celebration and purchasing habits are also included. Our results suggest Albertans spent over $7 million (retail) to buy 300,000 natural Christmas trees during the 1990 holiday season, and that 87% of the wholesale value or $2.6 million was spent to buy trees from outside of Alberta. Consumers would prefer to buy high-quality, locally-grown trees.
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Shen, Dan, Haipeng Shen, Shankar Bhamidi, Yolanda Muñoz Maldonado, Yongdai Kim, and J. S. Marron. "Functional Data Analysis of Tree Data Objects." Journal of Computational and Graphical Statistics 23, no. 2 (April 3, 2014): 418–38. http://dx.doi.org/10.1080/10618600.2013.786943.

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Kumazaki, R., and Y. Kunii. "APPLICATION OF 3D TREE MODELING USING POINT CLOUD DATA BY TERRESTRIAL LASER SCANNER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 995–1000. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-995-2020.

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Abstract. Constructing 3D models for trees such as those found in Japanese gardens, in which many species exist, requires the generation of tree shapes that combine the characteristics of the tree's species and natural diversity. Therefore, this study proposes a method for constructing a 3D tree model with highly-accurate tree shape reproducibility from tree point cloud data acquired by TLS. As a method, we attempted to construct a 3D tree model using the TreeQSM, which is open source for TLS-QSM method. However, in TreeQSM, since processing is based on the assumption that the tree point cloud consists of data related to trunks and branches, measuring trees in which leaves have fallen is recommended. To solve this problem, we proposed an efficient classification process that mainly uses thresholds for deviation and reflectance, which are the adjunct data of the object that can be acquired by laser measurement. Furthermore, to verify accuracy of the model, position coordinates from the constructed 3D tree model were extracted. The extracted coordinates were compared with the those of the tree point cloud data to clarify the extent to which the 3D tree model was constructed from the tree point cloud data. As a result, the 3D tree model was constructed within the standard deviation of 0.016 m from the tree point cloud data. Therefore, the reproducibility of the tree shape by the TLS-QSM method was also effective in terms of accuracy.
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Dissertations / Theses on the topic "Tree data"

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MacKinnon, Richard Kyle. "Seeing the forest for the trees: tree-based uncertain frequent pattern mining." Springer International Publishing, 2014. http://hdl.handle.net/1993/31059.

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Many frequent pattern mining algorithms operate on precise data, where each data point is an exact accounting of a phenomena (e.g., I have exactly two sisters). Alas, reasoning this way is a simplification for many real world observations. Measurements, predictions, environmental factors, human error, &ct. all introduce a degree of uncertainty into the mix. Tree-based frequent pattern mining algorithms such as FP-growth are particularly efficient due to their compact in-memory representations of the input database, but their uncertain extensions can require many more tree nodes. I propose new algorithms with tightened upper bounds to expected support, Tube-S and Tube-P, which mine frequent patterns from uncertain data. Extensive experimentation and analysis on datasets with different probability distributions are undertaken that show the tightness of my bounds in different situations.
February 2016
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Ahmad, Amir. "Data Transformation for Decision Tree Ensembles." Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.508528.

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Da, San Martino Giovanni <1979&gt. "Kernel Methods for Tree Structured Data." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1400/.

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Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of objects of a domain, the kernel function. Designing fast and good kernel functions is a challenging problem. In the case of tree structured data two issues become relevant: kernel for trees should not be sparse and should be fast to compute. The sparsity problem arises when, given a dataset and a kernel function, most structures of the dataset are completely dissimilar to one another. In those cases the classifier has too few information for making correct predictions on unseen data. In fact, it tends to produce a discriminating function behaving as the nearest neighbour rule. Sparsity is likely to arise for some standard tree kernel functions, such as the subtree and subset tree kernel, when they are applied to datasets with node labels belonging to a large domain. A second drawback of using tree kernels is the time complexity required both in learning and classification phases. Such a complexity can sometimes prevents the kernel application in scenarios involving large amount of data. This thesis proposes three contributions for resolving the above issues of kernel for trees. A first contribution aims at creating kernel functions which adapt to the statistical properties of the dataset, thus reducing its sparsity with respect to traditional tree kernel functions. Specifically, we propose to encode the input trees by an algorithm able to project the data onto a lower dimensional space with the property that similar structures are mapped similarly. By building kernel functions on the lower dimensional representation, we are able to perform inexact matchings between different inputs in the original space. A second contribution is the proposal of a novel kernel function based on the convolution kernel framework. Convolution kernel measures the similarity of two objects in terms of the similarities of their subparts. Most convolution kernels are based on counting the number of shared substructures, partially discarding information about their position in the original structure. The kernel function we propose is, instead, especially focused on this aspect. A third contribution is devoted at reducing the computational burden related to the calculation of a kernel function between a tree and a forest of trees, which is a typical operation in the classification phase and, for some algorithms, also in the learning phase. We propose a general methodology applicable to convolution kernels. Moreover, we show an instantiation of our technique when kernels such as the subtree and subset tree kernels are employed. In those cases, Direct Acyclic Graphs can be used to compactly represent shared substructures in different trees, thus reducing the computational burden and storage requirements.
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Liu, Dan. "Tree-based Models for Longitudinal Data." Bowling Green State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1399972118.

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Csank, Adam Z. "Research Communication: An International Tree-Ring Isotope Data Bank- A Proposed Repository For Tree-Ring Isotopic Data." Tree-Ring Society, 2009. http://hdl.handle.net/10150/622606.

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The International Tree-Ring Data Bank (ITRDB) is an invaluable resource, providing access to a massive and growing cache of tree-ring data. Oxygen, carbon, nitrogen and hydrogen isotope treering studies, which have provided valuable climatic and ecological information, have proliferated for decades so an ITRDB expansion to include isotopic data would likewise benefit the scientific community. An international tree-ring isotope databank (ITRIDB) would: (1) allow development of transfer functions from extended isotopic data sets, (2) provide abundant tree-ring isotopic data for meta-analysis, and (3) encourage isotopic network studies. A Europe network already exists, but the international data bank proposed here would constitute a de facto global network. Associated information to be incorporated into the database includes not only the customary ITRDB entries, but also elements peculiar to isotope chronologies. As with the current ITRDB, submission of data would be voluntary and as such it will be crucial to have the support of the tree-ring isotope community to contribute existing and forthcoming isotope series. The plan is to institute this isotope database in 2010, administered by the National Climatic Data Center.
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King, Stuart. "Optimizations and applications of Trie-Tree based frequent pattern mining." Diss., Connect to online resource - MSU authorized users, 2006.

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Thesis (M. S.)--Michigan State University. Dept. of Computer Science and Engineering, 2006.
Title from PDF t.p. (viewed on June 19, 2009) Includes bibliographical references (p. 79-80). Also issued in print.
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Rizo, David. "Symbolic music comparison with tree data structures." Doctoral thesis, Universidad de Alicante, 2010. http://hdl.handle.net/10045/18331.

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Evans, Margaret E. K., Donald A. Falk, Alexis Arizpe, Tyson L. Swetnam, Flurin Babst, and Kent E. Holsinger. "Fusing tree-ring and forest inventory data to infer influences on tree growth." WILEY, 2017. http://hdl.handle.net/10150/625361.

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Better understanding and prediction of tree growth is important because of the many ecosystem services provided by forests and the uncertainty surrounding how forests will respond to anthropogenic climate change. With the ultimate goal of improving models of forest dynamics, here we construct a statistical model that combines complementary data sources, tree-ring and forest inventory data. A Bayesian hierarchical model was used to gain inference on the effects of many factors on tree growth-individual tree size, climate, biophysical conditions, stand-level competitive environment, tree-level canopy status, and forest management treatments-using both diameter at breast height (dbh) and tree-ring data. The model consists of two multiple regression models, one each for the two data sources, linked via a constant of proportionality between coefficients that are found in parallel in the two regressions. This model was applied to a data set of similar to 130 increment cores and similar to 500 repeat measurements of dbh at a single site in the Jemez Mountains of north-central New Mexico, USA. The tree-ring data serve as the only source of information on how annual growth responds to climate variation, whereas both data types inform non-climatic effects on growth. Inferences from the model included positive effects on growth of seasonal precipitation, wetness index, and height ratio, and negative effects of dbh, seasonal temperature, southerly aspect and radiation, and plot basal area. Climatic effects inferred by the model were confirmed by a den-droclimatic analysis. Combining the two data sources substantially reduced uncertainty about non-climate fixed effects on radial increments. This demonstrates that forest inventory data measured on many trees, combined with tree-ring data developed for a small number of trees, can be used to quantify and parse multiple influences on absolute tree growth. We highlight the kinds of research questions that can be addressed by combining the high-resolution information on climate effects contained in tree rings with the rich tree-and stand-level information found in forest inventories, including projection of tree growth under future climate scenarios, carbon accounting, and investigation of management actions aimed at increasing forest resilience.
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Faustino, Bruno Filipe Fernandes Simões Salgueiro. "Implementation for spatial data of the shared nearest neighbour with metric data structures." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8489.

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Alizadeh, Khameneh Mohammad Amin. "Tree Detection and Species Identification using LiDAR Data." Thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-119269.

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The importance of single-tree-based information for forest management and related industries in countries like Sweden, which is covered in approximately 65% by forest, is the motivation for developing algorithms for tree detection and species identification in this study. Most of the previous studies in this field are carried out based on aerial and spectral images and less attention has been paid on detecting trees and identifying their species using laser points and clustering methods. In the first part of this study, two main approaches of clustering (hierarchical and K-means) are compared qualitatively in detecting 3-D ALS points that pertain to individual tree clusters. Further tests are performed on test sites using the supervised k-means algorithm in which the initial clustering points are defined as seed points. These points, which represent the top point of each tree are detected from the cross section analysis of the test area. Comparing those three methods (hierarchical, ordinary K-means and supervised K-means), the supervised K-means approach shows the best result for clustering single tree points. An average accuracy of 90% is achieved in detecting trees. Comparing the result of the thesis algorithms with results from the DPM software, developed by the Visimind Company for analysing LiDAR data, shows more than 85% match in detecting trees. Identification of trees is the second issue of this thesis work. For this analysis, 118 trees are extracted as reference trees with three species of spruce, pine and birch, which are the dominating species in Swedish forests. Totally six methods, including best fitted 3-D shapes (cone, sphere and cylinder) based on least squares method, point density, hull ratio and slope changes of tree outer surface are developed for identifying those species. The methods are applied on all extracted reference trees individually. For aggregating the results of all those methods, a fuzzy logic system is used because of its good reputation in combining fuzzy sets with no distinct boundaries. The best-obtained model from the fuzzy system provides 73%, 87% and 71% accuracies in identifying the birch, spruce and pine trees, respectively. The overall obtained accuracy in species categorization of trees is 77%, and this percentage is increased dealing with only coniferous and deciduous types classification. Classifying spruce and pine as coniferous versus birch as deciduous species, yielded to 84% accuracy.
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Books on the topic "Tree data"

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Hawgood, David. GEDCOM data transfer: Moving your family tree. 3rd ed. London: D. Hawgood, 1999.

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Hawgood, D. GEDCOM data transfer: Moving your family tree. London: Hawgood Computing, 1991.

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Oldfield, Jim. Your family tree. Grand Rapids, MI: Abacus, 1997.

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Sixsmith, Mark. Tree and timber data sheets: A guide to the characteristics of individual tree species. 3rd ed. Theale, Berkshire: Forestry Trust for Conservation and Education, 1995.

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Helm, Matthew. Family tree maker for dummies. Foster City, CA: IDG Books Worldwide, 2000.

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Helm, Matthew. Family tree maker for dummies. Foster City, CA: IDG Books Worldwide, 2000.

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Sameer, Mohammed Fazli Hussain. Genealogical table of Sri Lankan muslims: Family tree data. Colombo: Bits & Bytes, 1996.

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McClure, Rhonda R. The official Family tree maker 9. [Indianapolis, Ind.]: Premier Press, 2001.

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The official family tree maker, version 10: Fast & easy. Cincinnati, Ohio: Premier Press, 2002.

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Karsh, M. B. Guidelines for editing permanent sample plot data. St. John's, Nfld: Forestry Canada, Newfoundland and Labrador Region, 1993.

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Book chapters on the topic "Tree data"

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Bringmann, Björn, and Albrecht Zimmermann. "Tree 2 – Decision Trees for Tree Structured Data." In Knowledge Discovery in Databases: PKDD 2005, 46–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11564126_10.

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Beidler, John. "Tree Applications." In Data Structures and Algorithms, 217–50. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-1854-8_7.

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Allen, Grant, Bob Bryla, and Darl Kuhn. "Tree-Structured Data." In Oracle SQL Recipes, 313–33. Berkeley, CA: Apress, 2009. http://dx.doi.org/10.1007/978-1-4302-2510-2_13.

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Subero, Armstrong. "Tree Data Structures." In Codeless Data Structures and Algorithms, 31–40. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5725-8_3.

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Gama, João. "Oblique linear tree." In Advances in Intelligent Data Analysis Reasoning about Data, 187–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0052840.

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Tsang, Sidney, Yun Sing Koh, and Gillian Dobbie. "RP-Tree: Rare Pattern Tree Mining." In Data Warehousing and Knowledge Discovery, 277–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23544-3_21.

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Karimov, Elshad. "Binary Tree." In Data Structures and Algorithms in Swift, 77–86. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5769-2_10.

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Olson, David L., and Desheng Wu. "Regression Tree Models." In Predictive Data Mining Models, 45–54. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2543-3_5.

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Olson, David L., and Desheng Wu. "Regression Tree Models." In Predictive Data Mining Models, 57–77. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9664-9_5.

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Huang, Shuai, and Houtao Deng. "Abstraction Regression & Tree Models." In Data Analytics, 5–36. First edition. | Boca Raton : CRC Press, 2021.: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003102656-ch2.

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Conference papers on the topic "Tree data"

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Popovas, Darius, Valentas Mikalauskas, Dominykas Šlikas, Simonas Valotka, and Tautvydas Šorys. "Individual Tree Parameters Estimation from Terrestrial Laser Scanner Data." In Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.230.

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Tree models and information on the various characteristics of trees and forests are required for forest management, city models, carbon accounting and the management of assets. In order to get precise characteristics and information, tree modelling must be done at individual tree level as it represents the interaction process between trees. For sustainable forest management, more information is needed, however, the traditional methods of investigating forest parameters such as, tree height, diameter at breast height, crown diameter, stem curve and stem mapping or tree location are complex and labour-intensive. Light detection and ranging (LiDAR) has been proposed as a suitable technique for mapping of forest biomass. LiDAR can be operated in airborne configuration (Airborne laser scanning) or in a terrestrial setup. Terrestrial Laser Scanner measures forests from below canopy and offers a much more detailed description of the individual trees. The aim of this study is to derive the essential tree parameters for estimation of biomass from terrestrial LiDAR data. Tree height, diameter at breast height, crown diameter, stem curve and tree locations were extracted from Terrestrial Laser Scanner point clouds.
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Kourtellis, Nicolas, Gianmarco De Francisci Morales, Albert Bifet, and Arinto Murdopo. "VHT: Vertical hoeffding tree." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840687.

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Veness, Joel, Kee Siong Ng, Marcus Hutter, and Michael Bowling. "Context Tree Switching." In 2012 Data Compression Conference (DCC). IEEE, 2012. http://dx.doi.org/10.1109/dcc.2012.39.

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Veness, J., M. White, M. Bowling, and A. Gyorgy. "Partition Tree Weighting." In 2013 Data Compression Conference (DCC). IEEE, 2013. http://dx.doi.org/10.1109/dcc.2013.40.

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Desai, Ankit, and Sanjay Chaudhary. "Distributed decision tree v.2.0." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258011.

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Praveen Rao and Bongki Moon. "SketchTree: Approximate Tree Pattern Counts over Streaming Labeled Trees." In 22nd International Conference on Data Engineering (ICDE'06). IEEE, 2006. http://dx.doi.org/10.1109/icde.2006.141.

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Galakatos, Alex, Michael Markovitch, Carsten Binnig, Rodrigo Fonseca, and Tim Kraska. "FITing-Tree." In SIGMOD/PODS '19: International Conference on Management of Data. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3299869.3319860.

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O'Neill, Alexander, Marcus Hutter, Wen Shao, and Peter Sunehag. "Adaptive Context Tree Weighting." In 2012 Data Compression Conference (DCC). IEEE, 2012. http://dx.doi.org/10.1109/dcc.2012.38.

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Shun, Julian. "Parallel Wavelet Tree Construction." In 2015 Data Compression Conference (DCC). IEEE, 2015. http://dx.doi.org/10.1109/dcc.2015.7.

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Fang, Wenjing, Chaochao Chen, Bowen Song, Li Wang, Jun Zhou, and Kenny Q. Zhu. "Adapted Tree Boosting for Transfer Learning." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006028.

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Reports on the topic "Tree data"

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Berkman, Omer, and Uzi Vishkin. Recursive Star-Tree Parallel Data-Structure. Fort Belvoir, VA: Defense Technical Information Center, March 1990. http://dx.doi.org/10.21236/ada227803.

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Sewell, Christopher Meyer, James Paul Ahrens, Hamish Carr, and Gunther Weber. Data-Parallel Algorithm for Contour Tree Construction. Office of Scientific and Technical Information (OSTI), January 2017. http://dx.doi.org/10.2172/1340949.

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Loh, Wei-Yin. Tree-Structured Methods for Prediction and Data Visualization. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada499342.

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Luckman, B. H., and T. A. Innes. Tree-ring studies in Canada: a bibliography and data base. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1991. http://dx.doi.org/10.4095/131849.

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van Deusen, Paul C., and [Editor]. Analyses of Great Smoky Mountain Red Spruce Tree Ring Data. New Orleans, LA: U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, 1988. http://dx.doi.org/10.2737/so-gtr-69.

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Baughman, D. F., P. Hang, and C. S. Townsend. Waste Management Fault Tree Data Bank (WM): 1992 status report. Office of Scientific and Technical Information (OSTI), August 1993. http://dx.doi.org/10.2172/10159642.

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Johnson, T., and A. Colbrook. A Distributed Data-Balanced Dictionary Based on the B-Link Tree. Fort Belvoir, VA: Defense Technical Information Center, February 1992. http://dx.doi.org/10.21236/ada256841.

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Chang, LiWu, and James Tracy. Multi-Dimensional Inference and Confidential Data Protection with Decision Tree Methods. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada465156.

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Chang, LiWu. Statistical Sensitive Data Protection and Inference Prevention with Decision Tree Methods. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada465138.

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Byers, Loren W. Second Line of Defense Program Secondary Screening Field Operations Data--Concept Tree. Office of Scientific and Technical Information (OSTI), April 2013. http://dx.doi.org/10.2172/1073743.

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