Relevant bibliographies by topics / Growth pattern

Contents

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

Academic literature on the topic 'Growth pattern'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 February 2022

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Journal articles on the topic "Growth pattern"

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Han, Jiawei, and Jian Pei. "Mining frequent patterns by pattern-growth." ACM SIGKDD Explorations Newsletter 2, no. 2 (December 2000): 14–20. http://dx.doi.org/10.1145/380995.381002.

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Buchanan, Mark. "Pattern of growth." Nature Physics 8, no. 11 (November 2012): 775. http://dx.doi.org/10.1038/nphys2471.

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Kenmogne, Edith Belise. "The Impact of the Pattern-Growth Ordering on the Performances of Pattern Growth-Based Sequential Pattern Mining Algorithms." Computer and Information Science 10, no. 1 (December 25, 2016): 23. http://dx.doi.org/10.5539/cis.v10n1p23.

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Sequential Pattern Mining is an efficient technique for discovering recurring structures or patterns from very large datasetwidely addressed by the data mining community, with a very large field of applications, such as cross-marketing, DNA analysis, web log analysis,user behavior, sensor data, etc. The sequence pattern mining aims at extractinga set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan and PrefixSpan.In this paper, we are interested in the study of the impact of the pattern-growthordering on the performances of pattern growth-based sequential pattern mining algorithms.To this end, we introduce a class of pattern-growth orderings, called linear orderings, for which patterns are grown by making grow either the currentpattern prefix or the current pattern suffix from the same position at eachgrowth-step.We study the problem of pruning and partitioning the search space followinglinear orderings. Experimentations show that the order in which patternsgrow has a significant influence on the performances.
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Edith Belise, Kenmogne, Nkambou Roger, Tadmon Calvin, and Engelbert Mephu Nguifo. "A heuristic to predict the optimal pattern-growth direction for the pattern growth-based sequential pattern mining approach." Journal of Advanced Computer Science & Technology 6, no. 2 (June 4, 2017): 20. http://dx.doi.org/10.14419/jacst.v6i2.7011.

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Sequential pattern mining is an efficient technique for discovering recurring structures or patterns from very large datasets, with a very large field of applications. It aims at extracting a set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan, PrefixSpan and their further extensions. The performances of these algorithms depend on how patterns grow. Because of this, we introduce a heuristic to predict the optimal pattern-growth direction, i.e. the pattern-growth direction leading to the best performance in terms of runtime and memory usage. Then, we perform a number of experimentations on both real-life and synthetic datasets to test the heuristic. The performance analysis of these experimentations show that the heuristic prediction is reliable in general.
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Tsukahara, Tetsuo, Yoshie Shimoyama, Tomoki Ebata, Yukihiro Yokoyama, Tsuyoshi Igami, Gen Sugawara, Takashi Mizuno, Junpei Yamaguchi, Shigeo Nakamura, and Masato Nagino. "Cholangiocarcinoma with intraductal tubular growth pattern versus intraductal papillary growth pattern." Modern Pathology 29, no. 3 (January 15, 2016): 293–301. http://dx.doi.org/10.1038/modpathol.2015.152.

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Lander, Arthur D. "Pattern, Growth, and Control." Cell 144, no. 6 (March 2011): 955–69. http://dx.doi.org/10.1016/j.cell.2011.03.009.

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Jian Pei, Jiawei Han, B. Mortazavi-Asl, Jianyong Wang, H. Pinto, Qiming Chen, U. Dayal, and Mei-Chun Hsu. "Mining sequential patterns by pattern-growth: the PrefixSpan approach." IEEE Transactions on Knowledge and Data Engineering 16, no. 11 (November 2004): 1424–40. http://dx.doi.org/10.1109/tkde.2004.77.

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Isakkson, OGP, J.-O. Jansson, RG Clark, and I. Robinson. "Significance of the Secretory Pattern of Growth Hormone." Physiology 1, no. 2 (April 1, 1986): 44–47. http://dx.doi.org/10.1152/physiologyonline.1986.1.2.44.

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The plasma concentration of growth hormone fluctuates widely with pronounced peaks at intervals of a few hours and troughs of nearly vanishingly low concentrations in between. The pattern of secretion varies, and different patterns affect growth differently. Tall children usually have frequent growth hormone peaks of a high amplitude, whereas short, healthy children usually have fewer peaks of a lower amplitude. Male and female rats have different patterns, and a "masculine" pattern promotes growth more than a "feminine" pattern. If the same amount of growth hormone is administered in several pulses rather than continuously, the effect on growth is much greater.
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Eefsen, Rikke Løvendahl, Gert G. Van den Eynden, Gunilla Høyer-Hansen, Pnina Brodt, Ole Didrik Laerum, Peter B. Vermeulen, Ib Jarle Christensen, et al. "Histopathological Growth Pattern, Proteolysis and Angiogenesis in Chemonaive Patients Resected for Multiple Colorectal Liver Metastases." Journal of Oncology 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/907971.

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The purpose of this study was to characterise growth patterns, proteolysis, and angiogenesis in colorectal liver metastases from chemonaive patients with multiple liver metastases. Twenty-four patients were included in the study, resected for a median of 2.6 metastases. The growth pattern distribution was 25.8% desmoplastic, 33.9% pushing, and 21% replacement. In 20 patients, identical growth patterns were detected in all metastases, but in 8 of these patients, a second growth pattern was also present in one or two of the metastases. In the remaining 4 patients, no general growth pattern was observed, although none of the liver metastases included more than two growth patterns. Overall, a mixed growth pattern was demonstrated in 19.3% of the liver metastases. Compared to metastases with pushing, those with desmoplastic growth pattern had a significantly up-regulated expression of urokinase-type plasminogen activator receptor (P=0.0008). Angiogenesis was most pronounced in metastases with a pushing growth pattern in comparison to those with desmoplastic (P=0.0007) and replacement growth pattern (P=0.021). Although a minor fraction of the patients harboured metastases with different growth patterns, we observed a tendency toward growth pattern uniformity in the liver metastases arising in the same patient. The result suggests that the growth pattern of liver metastases is not a random phenomenon.
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Subasinghe, Shyamantha. "Urban Growth: From pixel to reality." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-353-2019.

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<p><strong>Abstract.</strong> Urban growth is a complex process created through the interaction of human and environmental conditions. The spatial configuration and dynamic process of urban growth is an important topic in contemporary geographical studies (Thapa and Murayama, 2010). However, urban growth pattern recognition is a challengeable task and it has become one of the major fields in Cartography. Since classical era of cartography, several methods have been employed in modelling and urban growth pattern recognition. It shows that there is no agreement among cartographer or any other spatial scientists on how to map the diverse patterns of urban growth.</p><p>Typical urban theories such as von Thünen’s (1826) bid-rent theory, Burgess’s (1925) concentric zone model, Christaller’s (1933) central place theory, and Hoyt’s (1939) sector model explain the urban structure in different manner. Most of them do not contribute to visualize the urban growth pattern spatiotemporally. Recently, by addressing this limitations, several sophisticated methods are used in urban growth visualization. Among them, morphological spatial pattern analysis (MSPA) is one of emerging raster data analysis methods which allows us to integrate neighbourhood interaction rules in urban growth pattern recognition and visualization. Angel et al. (2010) developed urban land classification (urban, suburban, rural, fringe open space, exterior open space, and rural open space) based on built and non-built land categories and detected three major types of urban growth (infill, extension, and leapfrog). However, developing urban land classifications using binary land use type and recognising only three types of urban growth pattern may be insufficient due to the existence of a higher complexity of urban growth. In such context, the present study introduce a geovisualization approach to map spatial patterns of urban growth using multiple land categories and develops three sub-levels of urban growth pattern for each major urban growth pattern.</p><p>The entire process of urban growth pattern recognition developed in this study can be summarized into three steps (Figure 1): (1) urban land mapping &amp;ndash; Landsat imageries representing two time points (2001 and 2017) were classified into two land categories (built and non-built) and developed into multiple classes using ancillary data, (2) recognizing three major patterns of urban growth (infill, extension, and leapfrog) &amp;ndash; the raster overlay method based on neighbourhood interaction rules, (3) development of sublevels of urban growth &amp;ndash; major three patterns were further developed and visualized nine urban growth patterns, namely low infill (LI), moderate infill (MI), high infill (HI), low extension (LE), moderate extension (ME), high extension (HE), low leapfrog (LL), moderate leapfrog (ML), and high leapfrog (HL). The developed procedure of this study in urban growth pattern recognition was tested using a case study of Colombo metropolitan area, Sri Lanka.</p>
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Dissertations / Theses on the topic "Growth pattern"

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Salehi, Taleghani Amir. "Entrepreneurial Growth Pattern : A Comparison Study on the Growth Pattern of Dotcoms vs. Brick-and-Mortars." Thesis, Umeå universitet, Företagsekonomi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-61934.

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Entrepreneurship is the foundation of the economic for each country. It has an inevitable impact onmicro- and macro-economic factors such as GDP, economic growth, employment/unemploymentrate, regional development, etc. Thus, entrepreneurial practices are crucial for each country in orderto have better economic conditions.Growth is the dominant part of entrepreneurial practices from which the success of small firms canbe assessed and evaluated. Firm’s growth involves different aspects such as motives, finance andownership strategies, indicators, and growth stimulus. These factors together provide a pattern ofgrowth that is different from one company to another.Since the advent of the Internet there has been changes in the business world and the terms such asdotcom, digital entrepreneurship, e-services, e-banking, etc. made a dramatic change in the way ofdoing business. Some companies were established based on the Internet and their income andexistence relied on the Internet. Some others on the other hand, use traditional method of businessbesides using the Internet as an extra tool.This study examines the small business growth pattern in order to find out how small firms grow.Furthermore, the difference between the growth pattern of digital firms and traditional companies isexamined to find out how the pattern of growth differs from dotcoms to the brick-and-mortars.This study is based on a qualitative research method with the approach of a case study research. Thecase study is designed on one major case to go deep while having four other supporting companiesin order to get the best results with the least subjectivity. The questionnaire was designed on a semistructureand the results were coded for the pattern. The questions were designed based on theconceptual framework which was changes based on the results and optimized.The results from this study provide a framework that gives a pattern of growth for small firms. Thesuggested framework of growth pattern has some major components: growth motive, growthstrategy, growth indicator, and growth stimulus. Furthermore, the research findings define the majordifferences between the growth pattern of dotcoms and brick-and-mortars.
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Teng, Jing. "Pattern formation and growth kinetics in eutectic systems." [Ames, Iowa : Iowa State University], 2007.

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Ali, Adnan. "Stochastic pattern formation in growth models with spatial competition." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/54323/.

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The field of stochastic growth encompasses various different processes which are ubiquitously seen across the physical world. In many systems, stochasticity appears quite naturally, where inherent randomness provides the right setting for the tone of motion and interaction, whose symphony leads to the surprising emergence of interesting patterns and structure. Although on the microscopic scale one can be overwhelmed by the randomness arising from the fluctuating interactions between components, on the macroscopic scale, however, one is mesmerized by the emergence of mathematical beauty and symmetry, leading to complex structures with fractal architecture. Competition between components adds an extra degree of complexity and leads to the possibility of critical behaviour and phase transitions. It is an important aspect of many systems, and in order to provide a full explanation of many natural phenomena, we have to understand the role it plays on modifying behaviour. The combination of stochastic growth and competition leads to the emergence of interesting complex patterns. They occur in various systems and in many forms, and thus we treat competition in growth models driven by different laws for the stochastic noise. As a consequence our results are widely applicable and we encourage the reader to find good use for them in their respective field. In this thesis we study stochastic systems containing interacting particles whose motion and interplay lead to directed growth structures on a particular geometry. We show how the effect of the overall geometry in many growth processes can be captured elegantly in terms of a time dependent metric. A natural example we treat is isoradial growth in two dimensions, with domain boundaries of competing microbial species as an example of a system with a homogeneously changing metric. In general, we view domain boundaries as space-time trajectories of particles moving on a dynamic surface and map those into more easily tractable systems with constant metric. This leads to establishing a generic relation between locally interacting, scale invariant stochastic space-time trajectories under constant and time dependent metric. Indeed “the book of nature is written in the language of mathematics” (Galileo Galilei) and we provide a mathematical framework for various systems with various interactions and our results are backed with numerical confirmation.
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Chintaradeja, Varavuth. "Documenting state employment pattern and growth during business cycles /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144407.

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Kufimfutu, Bakelana ba. "Crop planting pattern effects on crop and weed growth /." The Ohio State University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487778663287222.

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Gjorgjieva, Julijana. "Turing Pattern Dynamics for Spatiotemporal Models with Growth and Curvature." Scholarship @ Claremont, 2006. https://scholarship.claremont.edu/hmc_theses/181.

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Turing theory plays an important role in real biological pattern formation problems, such as solid tumor growth and animal coat patterns. To understand how patterns form and develop over time due to growth, we consider spatiotemporal patterns, in particular Turing patterns, for reaction diffusion systems on growing surfaces with curvature. Of particular interest is isotropic growth of the sphere, where growth of the domain occurs in the same proportion in all directions. Applying a modified linear stability analysis and a separation of timescales argument, we derive the necessary and sufficient conditions for a diffusion driven instability of the steady state and for the emergence of spatial patterns. Finally, we explore these results using numerical simulations.
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Numazawa, Satoshi. "Modeling of metal nanocluster growth on patterned substrates and surface pattern formation under ion bombardment." Forschungszentrum Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:d120-qucosa-93652.

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This thesis addresses the metal nanocluster growth process on prepatterned substrates, the development of atomistic simulation method with respect to an acceleration of the atomistic transition states, and the continuum model of the ion-beam inducing semiconductor surface pattern formation mechanism. Experimentally, highly ordered Ag nanocluster structures have been grown on pre-patterned amorphous SiO^2 surfaces by oblique angle physical vapor deposition at room temperature. Despite the small undulation of the rippled surface, the stripe-like Ag nanoclusters are very pronounced, reproducible and well-separated. The first topic is the investigation of this growth process with a continuum theoretical approach to the surface gas condensation as well as an atomistic cluster growth model. The atomistic simulation model is a lattice-based kinetic Monte-Carlo (KMC) method using a combination of a simplified inter-atomic potential and experimental transition barriers taken from the literature. An effective transition event classification method is introduced which allows a boost factor of several thousand compared to a traditional KMC approach, thus allowing experimental time scales to be modeled. The simulation predicts a low sticking probability for the arriving atoms, millisecond order lifetimes for single Ag monomers and ≈1 nm square surface migration ranges of Ag monomers. The simulations give excellent reproduction of the experimentally observed nanocluster growth patterns. The second topic specifies the acceleration scheme utilized in the metallic cluster growth model. Concerning the atomistic movements, a classical harmonic transition state theory is considered and applied in discrete lattice cells with hierarchical transition levels. The model results in an effective reduction of KMC simulation steps by utilizing a classification scheme of transition levels for thermally activated atomistic diffusion processes. Thermally activated atomistic movements are considered as local transition events constrained in potential energy wells over certain local time periods. These processes are represented by Markov chains of multi-dimensional Boolean valued functions in three dimensional lattice space. The events inhibited by the barriers under a certain level are regarded as thermal fluctuations of the canonical ensemble and accepted freely. Consequently, the fluctuating system evolution process is implemented as a Markov chain of equivalence class objects. It is shown that the process can be characterized by the acceptance of metastable local transitions. The method is applied to a problem of Au and Ag cluster growth on a rippled surface. The simulation predicts the existence of a morphology dependent transition time limit from a local metastable to stable state for subsequent cluster growth by accretion. The third topic is the formation of ripple structures on ion bombarded semiconductor surfaces treated in the first topic as the prepatterned substrate of the metallic deposition. This intriguing phenomenon has been known since the 1960\'s and various theoretical approaches have been explored. These previous models are discussed and a new non-linear model is formulated, based on the local atomic flow and associated density change in the near surface region. Within this framework ripple structures are shown to form without the necessity to invoke surface diffusion or large sputtering as important mechanisms. The model can also be extended to the case where sputtering is important and it is shown that in this case, certain \\lq magic\' angles can occur at which the ripple patterns are most clearly defined. The results including some analytic solutions of the nonlinear equation of motions are in very good agreement with experimental observation.
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Zhang, Qi. "The Application of Sequential Pattern Mining in Healthcare Workflow System and an Improved Mining Algorithm Based on Pattern-Growth Approach." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378113261.

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Zerkoune, Mohammed A. "Effect of Messenger® on Cantaloupe Growth Pattern and Yield." College of Agriculture and Life Sciences, University of Arizona (Tucson, AZ), 2002. http://hdl.handle.net/10150/214943.

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An experimental site was selected at Yuma Agricultural Research Center, University of Arizona, to evaluate the effect of Messenger on melon plant growth and yield. Four treatments in completely randomized block design replicated four times were applied to melon planted on 84- inch beds. High Mark open pollinated melon variety was planted on 3-20-2001 using a commercial planter. Treatments included Messenger applied at 3-leaf stage on 5-4-2001 Messenger applied every 14 days starting on 5-15, 5-29, 6-13, 6-27-2001, standard management practices and control. Observations collected included plant mapping and yield. Results were variable, showed no significant effect of Messenger on plant growth and yield. However, there was indication that Messenger may have an effect on plant growth pattern and yield. Messenger applied at 3-leaf stage and repeated applications seemed to induce an early melon formation, increased number of nodes and yield. It was not possible to make recommendation based on one-year results. Further investigation is needed to verify the results obtained from this experiment.
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Wasserman, Jonathan Daniel. "Pattern formation in Drosophila : roles of the EGF receptor pathway." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624214.

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Books on the topic "Growth pattern"

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Growth patterns, dynamic branches: A contribution to the description of the socialist growth pattern. Budapest: Akadémiai Kiadó, 1988.

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New visions on form and growth: Fingered growth, dendrites, and flames. Oxford: Oxford University Press, 2004.

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Society for Developmental Biology. Symposium. Genetics of pattern formation and growth control. Edited by Mahowald Anthony P. New York: Wiley-Liss, 1990.

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Pattern formation in diffusion-limited crystal growth. Singapore: World Scientific, 1996.

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Jamtveit, Bjørn, and Paul Meakin, eds. Growth, Dissolution and Pattern Formation in Geosystems. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9179-9.

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Spatial urban pattern and growth of urbanisation. New Delhi, India: Inter-India Publications, 1986.

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Kay, Neil M. Pattern in corporate evolution. Oxford [England]: Oxford University Press, 1997.

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Cycles of becoming: The planetary pattern of growth. 2nd ed. Santa Monica, Calif: Earthwalk School of Astrology Pub., 2005.

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1941-, Stanley H. Eugene, Ostrowsky Nicole 1943-, Institut d'études scientifiques de Cargèse., and North Atlantic Treaty Organization. Scientific Affairs Division., eds. Random fluctuations and pattern growth: Experiments and models. Dordrecht [Netherlands]: Kluwer Academic Publishers, 1988.

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Public expenditure in Nepal: Growth pattern and impact. New Delhi: Sterling Publishers, 1988.

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Book chapters on the topic "Growth pattern"

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Han, Jiawei, and Jian Pei. "Pattern-Growth Methods." In Frequent Pattern Mining, 65–81. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_3.

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Cheng, Hong, and Jiawei Han. "Pattern-Growth Methods." In Encyclopedia of Database Systems, 2692–96. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_263.

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Cheng, Hong, and Jiawei Han. "Pattern-Growth Methods." In Encyclopedia of Database Systems, 2051–54. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_263.

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Cheng, Hong, and Jiawei Han. "Pattern-Growth Methods." In Encyclopedia of Database Systems, 1–5. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_263-2.

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Misbah, C., H. Müller-Krumbhaar, Y. Saito, and D. E. Temkin. "Pattern Formation in Directional Solidification." In Growth and Form, 167–75. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4684-1357-1_16.

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Deutsch, Andreas, and Sabine Dormann. "Growth Processes." In Cellular Automaton Modeling of Biological Pattern Formation, 203–17. Boston, MA: Birkhäuser Boston, 2017. http://dx.doi.org/10.1007/978-1-4899-7980-3_9.

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Goudriaan, J., and H. H. Van Laar. "The main seasonal growth pattern." In Modelling Potential Crop Growth Processes, 7–28. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0750-1_2.

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Michely, Thomas, and Joachim Krug. "Pattern Formation in Multilayer Growth." In Islands, Mounds and Atoms, 121–226. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18672-1_4.

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Han, J., J. Pei, and X. Yan. "Sequential Pattern Mining by Pattern-Growth: Principles and Extensions*." In Foundations and Advances in Data Mining, 183–220. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11362197_8.

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Wang, Chen, Mingsheng Hong, Jian Pei, Haofeng Zhou, Wei Wang, and Baile Shi. "Efficient Pattern-Growth Methods for Frequent Tree Pattern Mining." In Advances in Knowledge Discovery and Data Mining, 441–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24775-3_54.

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Conference papers on the topic "Growth pattern"

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Stoycheva, Milena, and Petko Ruskov. "Growth mindset development pattern." In EuroPLoP 2015: 20th European Conference on Pattern Languages of Programs. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2855321.2855329.

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Al-Hamodi, Arkan A. G., and Songfeng Lu. "MRFP: Discovery Frequent Patterns Using MapReduce Frequent Pattern Growth." In 2016 International Conference on Network and Information Systems for Computers (ICNISC). IEEE, 2016. http://dx.doi.org/10.1109/icnisc.2016.071.

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Bashir, Shariq, Zahid Halim, and A. Rauf Baig. "Mining fault tolerant frequent patterns using pattern growth approach." In 2008 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA). IEEE, 2008. http://dx.doi.org/10.1109/aiccsa.2008.4493532.

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Xiao, Bo, Liang Zhang, Qianfang Xu, and Jun Guo. "Mining Maximal Hyperclique Pattern: A Hyperclique Pattern Growth Strategy." In 2008 International Seminar on Business and Information Management (ISBIM). IEEE, 2008. http://dx.doi.org/10.1109/isbim.2008.60.

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Cheddad, Abbas, Huseyin Kusetogullari, and Hakan Grahn. "Object recognition using shape growth pattern." In 2017 10th International Symposium on Image and Signal Processing and Analysis (ISPA). IEEE, 2017. http://dx.doi.org/10.1109/ispa.2017.8073567.

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Rana, D. P., N. J. Mistry, and M. M. Raghuwanshi. "Memory boosting for pattern growth approach." In 2014 International Conference on Information Science, Electronics and Electrical Engineering (ISEEE). IEEE, 2014. http://dx.doi.org/10.1109/infoseee.2014.6946262.

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Patel, Roshani, and Tarunika Chaudhari. "A review on sequential pattern mining using pattern growth approach." In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2016. http://dx.doi.org/10.1109/wispnet.2016.7566371.

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Hamdi, Shah Muhammad, Berkay Aydin, and Rafal A. Angryk. "A Pattern Growth-Based Approach for Mining Spatiotemporal Co-occurrence Patterns." In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0162.

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Lakkshmanan, Ajanthaa, A. Abirami Shri, and E. Aruna. "Pattern classification for finding facial growth abnormalities." In 2013 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2013. http://dx.doi.org/10.1109/iccic.2013.6724126.

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Akbar, Monika, and Rafal A. Angryk. "Frequent pattern-growth approach for document organization." In Proceeding of the 2nd international workshop. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1458484.1458496.

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Reports on the topic "Growth pattern"

1

Teng, Jing. Pattern Formation and Growth Kinetics in Eutectic Systems. Office of Scientific and Technical Information (OSTI), January 2007. http://dx.doi.org/10.2172/933125.

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2

Stoica, Gerald E. Gene Regulation and Expression Pattern of the Growth Factor Pleiotrophin in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada420767.

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3

McDonald, Hannah. Tree Growth and Spatial Pattern in Two Forest Park Permanent Plots: A Look at Stand Composition and Condition. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.313.

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4

Eskelinen, Heikki, Timo Lautanen, Pasi Saukkonen, Olaf Foss, Frants Gundersen, Mats Johansson, and Marcus Adolphson. Urban Patterns of Growth. Edited by Niels Boje Groth and Lars Winther. Nordic Council of Ministers, February 2013. http://dx.doi.org/10.6027/tn2013-508.

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5

Keller, Wolfgang. How Trade Patterns and Technology Flows Affect Productivity Growth. Cambridge, MA: National Bureau of Economic Research, March 1999. http://dx.doi.org/10.3386/w6990.

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6

Alban, David H., Donald H. Prettyman, and Gary J. Brand. Growth patterns of red pine on fine-textured soils. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station, 1987. http://dx.doi.org/10.2737/nc-rp-280.

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7

Feenstra, Robert, and Andrew Rose. Putting Things in Order: Patterns of Trade Dynamics and Growth. Cambridge, MA: National Bureau of Economic Research, March 1997. http://dx.doi.org/10.3386/w5975.

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8

Bellerby, Linda. Patterns of information system growth in community mental health centers. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.223.

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9

Klette, Tor Jakob, and Zvi Griliches. Empirical Patterns of Firm Growth and R&D Investment: A QuUality LadderModel Interpretation. Cambridge, MA: National Bureau of Economic Research, October 1998. http://dx.doi.org/10.3386/w6753.

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10

Klette, Tor Jakob, and Zvi Griliches. Empirical Patterns of Firm Growth and R&D Investment: A Quality Ladder Model Interpretation. Cambridge, MA: National Bureau of Economic Research, February 1997. http://dx.doi.org/10.3386/w5945.

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