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Добірка наукової літератури з теми "Approximate Mining"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Approximate Mining"

Livshits, Ester, Alireza Heidari, Ihab F. Ilyas, and Benny Kimelfeld. "Approximate denial constraints." Proceedings of the VLDB Endowment 13, no. 10 (June 2020): 1682–95. http://dx.doi.org/10.14778/3401960.3401966.

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The problem of mining integrity constraints from data has been extensively studied over the past two decades for commonly used types of constraints, including the classic Functional Dependencies (FDs) and the more general Denial Constraints (DCs). In this paper, we investigate the problem of mining from data approximate DCs, that is, DCs that are "almost" satisfied. Approximation allows us to discover more accurate constraints in inconsistent databases and detect rules that are generally correct but may have a few exceptions. It also allows to avoid overfitting and obtain constraints that are more general, more natural, and less contrived. We introduce the algorithm ADCMiner for mining approximate DCs. An important feature of this algorithm is that it does not assume any specific approximation function for DCs, but rather allows for arbitrary approximation functions that satisfy some natural axioms that we define in the paper. We also show how our algorithm can be combined with sampling to return highly accurate results considerably faster.

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Yip, Kelly K., and David A. Nembhard. "Mining approximate sequential patterns with gaps." International Journal of Data Mining, Modelling and Management 7, no. 2 (2015): 108. http://dx.doi.org/10.1504/ijdmmm.2015.069249.

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Combi, Carlo, and Pietro Sala. "Mining approximate interval-based temporal dependencies." Acta Informatica 53, no. 6-8 (September 14, 2015): 547–85. http://dx.doi.org/10.1007/s00236-015-0246-x.

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Chen, Yan, and Aijun An. "Approximate Parallel High Utility Itemset Mining." Big Data Research 6 (December 2016): 26–42. http://dx.doi.org/10.1016/j.bdr.2016.07.001.

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Su, Na, Zhe Hui Wu, Ji Min Liu, Tai An Liu, Xin Jun An, and Chang Qing Yan. "Mining Approximate Frequent Itemsets over Data Streams." Applied Mechanics and Materials 685 (October 2014): 536–39. http://dx.doi.org/10.4028/www.scientific.net/amm.685.536.

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This paper proposes a method based on Lossy Counting to mine frequent itemsets. Logarithmic tilted time window is adopted to emphasize the importance of recent data. Multilayer count queue framework is used to avoid the counter overflowing and query top-Kitemsets quickly using a index table.

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Silvestri, Claudio, and Salvatore Orlando. "Approximate mining of frequent patterns on streams." Intelligent Data Analysis 11, no. 1 (March 15, 2007): 49–73. http://dx.doi.org/10.3233/ida-2007-11104.

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McCoy, Corren G., Michael L. Nelson, and Michele C. Weigle. "Mining the Web to approximate university rankings." Information Discovery and Delivery 46, no. 3 (August 20, 2018): 173–83. http://dx.doi.org/10.1108/idd-05-2018-0014.

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Purpose The purpose of this study is to present an alternative to university ranking lists published in U.S. News & World Report, Times Higher Education, Academic Ranking of World Universities and Money Magazine. A strategy is proposed to mine a collection of university data obtained from Twitter and publicly available online academic sources to compute social media metrics that approximate typical academic rankings of US universities. Design/methodology/approach The Twitter application programming interface (API) is used to rank 264 universities using two easily collected measurements. The University Twitter Engagement (UTE) score is the total number of primary and secondary followers affiliated with the university. The authors mine other public data sources related to endowment funds, athletic expenditures and student enrollment to compute a ranking based on the endowment, expenditures and enrollment (EEE) score. Findings In rank-to-rank comparisons, the authors observed a significant, positive rank correlation (τ = 0.6018) between UTE and an aggregate reputation ranking, which indicates UTE could be a viable proxy for ranking atypical institutions normally excluded from traditional lists. Originality/value The UTE and EEE metrics offer distinct advantages because they can be calculated on-demand rather than relying on an annual publication and they promote diversity in the ranking lists, as any university with a Twitter account can be ranked by UTE and any university with online information about enrollment, expenditures and endowment can be given an EEE rank. The authors also propose a unique approach for discovering official university accounts by mining and correlating the profile information of Twitter friends.

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Mazlack, Lawrence J. "Approximate reasoning applied to unsupervised database mining." International Journal of Intelligent Systems 12, no. 5 (May 1997): 391–414. http://dx.doi.org/10.1002/(sici)1098-111x(199705)12:5<391::aid-int3>3.0.co;2-i.

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Bashir, Shariq, and Daphne Teck Ching Lai. "Mining Approximate Frequent Itemsets Using Pattern Growth Approach." Information Technology and Control 50, no. 4 (December 16, 2021): 627–44. http://dx.doi.org/10.5755/j01.itc.50.4.29060.

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Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional frequent itemset mining. This is because the AFI mining algorithms generate large number of candidate itemsets. This article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution of the proposed approach is it mines core patterns and examines approximate conditions of candidate AFIs directly with single phase and two full scans of database. Related algorithms apply Apriori-based candidate generation and test approach and require multiple phases to obtain complete AFIs. First phase generates core patterns, and second phase examines approximate conditions of core patterns. Specifically, the article proposes novel techniques that how to map transactions on approximate FP-tree, and how to mine AFIs from the conditional patterns of approximate FP-tree. The approximate FP-tree maps transactions on shared branches when the transactions share a similar set of items. This reduces the size of databases and helps to efficiently compute the approximate conditions of candidate itemsets. We compare the performance of our algorithm with the state of the art AFI mining algorithms on benchmark databases. The experiments are analyzed by comparing the processing time of algorithms and scalability of algorithms on varying database size and transaction length. The results show pattern growth approach mines AFIs in less processing time than related Apriori-based algorithms.

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CHEN, Siyu, Ning WANG, and Mengmeng ZHANG. "Mining Approximate Primary Functional Dependency on Web Tables." IEICE Transactions on Information and Systems E102.D, no. 3 (March 1, 2019): 650–54. http://dx.doi.org/10.1587/transinf.2018edl8130.

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Дисертації з теми "Approximate Mining"

Kolli, Lakshmi Priya. "Mining for Frequent Community Structures using Approximate Graph Matching." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1623166375110273.

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Agarwal, Khushbu. "A partition based approach to approximate tree mining a memory hierarchy perspective /." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1196284256.

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Agarwal, Khushbu. "A partition based approach to approximate tree mining : a memory hierarchy perspective." The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1196284256.

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Carraher, Lee A. "Approximate Clustering Algorithms for High Dimensional Streaming and Distributed Data." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1511860805777818.

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Fuhry, David P. "PLASMA-HD: Probing the LAttice Structure and MAkeup of High-dimensional Data." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440431146.

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Sartipi, Kamran. "Software Architecture Recovery based on Pattern Matching." Thesis, University of Waterloo, 2003. http://hdl.handle.net/10012/1122.

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Pattern matching approaches in reverse engineering aim to incorporate domain knowledge and system documentation in the software architecture extraction process, hence provide a user/tool collaborative environment for architectural design recovery. This thesis presents a model and an environment for recovering the high level design of legacy software systems based on user defined architectural patterns and graph matching techniques. In the proposed model, a high-level view of a software system in terms of the system components and their interactions is represented as a query, using a description language. A query is mapped onto a pattern-graph, where a module and its interactions with other modules are represented as a group of graph-nodes and a group of graph-edges, respectively. Interaction constraints can be modeled by the description language as a part of the query. Such a pattern-graph is applied against an entity-relation graph that represents the information extracted from the source code of the software system. An approximate graph matching process performs a series of graph edit operations (i. e. , node/edge insertion/deletion) on the pattern-graph and uses a ranking mechanism based on data mining association to obtain a sub-optimal solution. The obtained solution corresponds to an extracted architecture that complies with the given query. An interactive prototype toolkit implemented as part of this thesis provides an environment for architecture recovery in two levels. First the system is decomposed into a number of subsystems of files. Second each subsystem can be decomposed into a number of modules of functions, datatypes, and variables.

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Kuo, Fang-Chen, and 郭芳甄. "Mining Approximate Frequent Itemsets from Noisy Data." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/6waagh.

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碩士
東吳大學
資訊科學系
96
To discover association rules, frequent itemset mining can find out items that appear frequently together in a dataset and to be the first step in the analysis of data arising in broad range of application. Moreover, industry can use mining result to improve marketing strategy and profitability. Traditional frequent itemset mining utilizes the “exact” mode. However, the exact-mode mining is not appropriate for real data. Mining noisy data using the exact mode cannot generate correct frequent itemsets, and may eventually lead to incorrect decisions. In recent years, many researchers have studied how to discover frequent itemsets from noisy data. However, existing methods can become inefficient when the dataset is sparse. Therefore, these methods cannot be applied to all kinds of datasets. In this paper, we propose a new algorithm, called the TAFI algorithm, for mining approximate frequent itemsets. The TAFI algorithm not only can correctly and efficiently discover approximate frequent itemsets from noisy data, but also can perform well with spare datasets.

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Mantovani, Matteo. "Approximate Data Mining Techniques on Clinical Data." Doctoral thesis, 2020. http://hdl.handle.net/11562/1018039.

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The past two decades have witnessed an explosion in the number of medical and healthcare datasets available to researchers and healthcare professionals. Data collection efforts are highly required, and this prompts the development of appropriate data mining techniques and tools that can automatically extract relevant information from data. Consequently, they provide insights into various clinical behaviors or processes captured by the data. Since these tools should support decision-making activities of medical experts, all the extracted information must be represented in a human-friendly way, that is, in a concise and easy-to-understand form. To this purpose, here we propose a new framework that collects different new mining techniques and tools proposed. These techniques mainly focus on two aspects: the temporal one and the predictive one. All of these techniques were then applied to clinical data and, in particular, ICU data from MIMIC III database. It showed the flexibility of the framework, which is able to retrieve different outcomes from the overall dataset. The first two techniques rely on the concept of Approximate Temporal Functional Dependencies (ATFDs). ATFDs have been proposed, with their suitable treatment of temporal information, as a methodological tool for mining clinical data. An example of the knowledge derivable through dependencies may be "within 15 days, patients with the same diagnosis and the same therapy usually receive the same daily amount of drug". However, current ATFD models are not analyzing the temporal evolution of the data, such as "For most patients with the same diagnosis, the same drug is prescribed after the same symptom". To this extent, we propose a new kind of ATFD called Approximate Pure Temporally Evolving Functional Dependencies (APEFDs). Another limitation of such kind of dependencies is that they cannot deal with quantitative data when some tolerance can be allowed for numerical values. In particular, this limitation arises in clinical data warehouses, where analysis and mining have to consider one or more measures related to quantitative data (such as lab test results and vital signs), concerning multiple dimensional (alphanumeric) attributes (such as patient, hospital, physician, diagnosis) and some time dimensions (such as the day since hospitalization and the calendar date). According to this scenario, we introduce a new kind of ATFD, named Multi-Approximate Temporal Functional Dependency (MATFD), which considers dependencies between dimensions and quantitative measures from temporal clinical data. These new dependencies may provide new knowledge as "within 15 days, patients with the same diagnosis and the same therapy receive a daily amount of drug within a fixed range". The other techniques are based on pattern mining, which has also been proposed as a methodological tool for mining clinical data. However, many methods proposed so far focus on mining of temporal rules which describe relationships between data sequences or instantaneous events, without considering the presence of more complex temporal patterns into the dataset. These patterns, such as trends of a particular vital sign, are often very relevant for clinicians. Moreover, it is really interesting to discover if some sort of event, such as a drug administration, is capable of changing these trends and how. To this extent, we propose a new kind of temporal patterns, called Trend-Event Patterns (TEPs), that focuses on events and their influence on trends that can be retrieved from some measures, such as vital signs. With TEPs we can express concepts such as "The administration of paracetamol on a patient with an increasing temperature leads to a decreasing trend in temperature after such administration occurs". We also decided to analyze another interesting pattern mining technique that includes prediction. This technique discovers a compact set of patterns that aim to describe the condition (or class) of interest. Our framework relies on a classification model that considers and combines various predictive pattern candidates and selects only those that are important to improve the overall class prediction performance. We show that our classification approach achieves a significant reduction in the number of extracted patterns, compared to the state-of-the-art methods based on minimum predictive pattern mining approach, while preserving the overall classification accuracy of the model. For each technique described above, we developed a tool to retrieve its kind of rule. All the results are obtained by pre-processing and mining clinical data and, as mentioned before, in particular ICU data from MIMIC III database.

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Liu, Chunyang. "Summarizing data with representative patterns." Thesis, 2016. http://hdl.handle.net/10453/52923.

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University of Technology Sydney. Faculty of Engineering and Information Technology.
The advance of technology makes data acquisition and storage become unprecedentedly convenient. It contributes to the rapid growth of not only the volume but also the veracity and variety of data in recent years, which poses new challenges to the data mining area. For example, uncertain data mining emerges due to its capability to model the inherent veracity of data; spatial data mining attracts much research attention as the widespread of location-based services and wearable devices. As a fundamental topic of data mining, how to effectively and efficiently summarize data in this situation still remains to be explored. This thesis studied the problem of summarizing data with representative patterns. The objective is to find a set of patterns, which is much more concise but still contains rich information of the original data, and may provide valuable insights for further analysis of data. In the light of this idea, we formally formulate the problem and provide effective and efficient solutions in various scenarios. We study the problem of summarizing probabilistic frequent patterns over uncertain data. Probabilistic frequent pattern mining over uncertain data has received much research attention due to the wide applicabilities of uncertain data. It suffers from the problem of generating an exponential number of result patterns, which hinders the analysis of patterns and calls for the need to find a small number of representative patterns to approximate all other patterns. We formally formulate the problem of probabilistic representative frequent pattern (P-RFP) mining, which aims to find the minimal set of patterns with sufficiently high probability to represent all other patterns. The bottleneck turns out to be checking whether a pattern can probabilistically represent another, which involves the computation of a joint probability of the supports of two patterns. We propose a novel dynamic programming-based approach to address the problem and devise effective optimization strategies to improve the computation efficiency. To enhance the practicability of P-RFP mining, we introduce a novel approximation of the joint probability with both theoretical and empirical proofs. Based on the approximation, we propose an Approximate P-RFP Mining (APM) algorithm, which effectively and efficiently compresses the probabilistic frequent pattern set. The error rate of APM is guaranteed to be very small when the database contains hundreds of transactions, which further affirms that APM is a practical solution for summarizing probabilistic frequent patterns. We address the problem of directly summarizing uncertain transaction database by formulating the problem as Minimal Probabilistic Tile Cover Mining, which aims to find a high-quality probabilistic tile set covering an uncertain database with minimal cost. We define the concept of Probabilistic Price and Probabilistic Price Order to evaluate and compare the quality of tiles, and propose a framework to discover the minimal probabilistic tile cover. The bottleneck is to check whether a tile is better than another according to the Probabilistic Price Order, which involves the computation of a joint probability. We prove that it can be decomposed into independent terms and calculated efficiently. Several optimization techniques are devised to further improve the performance. We analyze the problem of summarizing co-locations mined from spatial databases. Co-location pattern mining finds patterns of spatial features whose instances tend to locate together in geographic space. However, the traditional framework of co-location pattern mining produces an exponential number of patterns because of the downward closure property, which makes it difficult for users to understand, assess or apply the huge number of resulted patterns. To address this issue, we study the problem of mining representative co-location patterns (RCP). We first define a covering relationship between two co-location patterns then formally formulate the problem of Representative Co-location Pattern mining. To solve the problem of RCP mining, we propose the RCPFast algorithm adopting the post-mining framework and the RCPMS algorithm pushing pattern summarization into the co-location mining process.

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董原賓. "Approximately mining frequent representative itemsets on data streams." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/31159769915805928034.

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Книги з теми "Approximate Mining"

Henry G, Burnett, and Bret Louis-Alexis. Part I Host States, Mining Companies, and Mining Projects, 2 Mining Companies. Oxford University Press, 2017. http://dx.doi.org/10.1093/law/9780198757641.003.0002.

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Mining companies are corporations or partnerships primarily involved in the exploration or production of metal or mineral deposits. There are approximately 2,100 mining companies in the world today, 100 of which are referred to as majors and 200 as mid-tier. Approximately 1,700 junior mining companies (referred to as juniors) constitute the vast majority of mining companies in existence today. These juniors are typically focused on mining exploration and often do not generate revenues. Finally, approximately 80 State-owned national mining companies (NMCs) play a significant role in the global mining industry. This chapter discusses each of these four categories of mining companies in detail, in relation to their respective focus, risks undertaken, and types of investment they attract and disputes in which they may find themselves involved.

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Sobczyk, Eugeniusz Jacek. Uciążliwość eksploatacji złóż węgla kamiennego wynikająca z warunków geologicznych i górniczych. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN, 2022. http://dx.doi.org/10.33223/onermin/0222.

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Hard coal mining is characterised by features that pose numerous challenges to its current operations and cause strategic and operational problems in planning its development. The most important of these include the high capital intensity of mining investment projects and the dynamically changing environment in which the sector operates, while the long-term role of the sector is dependent on factors originating at both national and international level. At the same time, the conditions for coal mining are deteriorating, the resources more readily available in active mines are being exhausted, mining depths are increasing, temperature levels in pits are rising, transport routes for staff and materials are getting longer, effective working time is decreasing, natural hazards are increasing, and seams with an increasing content of waste rock are being mined. The mining industry is currently in a very difficult situation, both in technical (mining) and economic terms. It cannot be ignored, however, that the difficult financial situation of Polish mining companies is largely exacerbated by their high operating costs. The cost of obtaining coal and its price are two key elements that determine the level of efficiency of Polish mines. This situation could be improved by streamlining the planning processes. This would involve striving for production planning that is as predictable as possible and, on the other hand, economically efficient. In this respect, it is helpful to plan the production from operating longwalls with full awareness of the complexity of geological and mining conditions and the resulting economic consequences. The constraints on increasing the efficiency of the mining process are due to the technical potential of the mining process, organisational factors and, above all, geological and mining conditions. The main objective of the monograph is to identify relations between geological and mining parameters and the level of longwall mining costs, and their daily output. In view of the above, it was assumed that it was possible to present the relationship between the costs of longwall mining and the daily coal output from a longwall as a function of onerous geological and mining factors. The monograph presents two models of onerous geological and mining conditions, including natural hazards, deposit (seam) parameters, mining (technical) parameters and environmental factors. The models were used to calculate two onerousness indicators, Wue and WUt, which synthetically define the level of impact of onerous geological and mining conditions on the mining process in relation to: —— operating costs at longwall faces – indicator WUe, —— daily longwall mining output – indicator WUt. In the next research step, the analysis of direct relationships of selected geological and mining factors with longwall costs and the mining output level was conducted. For this purpose, two statistical models were built for the following dependent variables: unit operating cost (Model 1) and daily longwall mining output (Model 2). The models served two additional sub-objectives: interpretation of the influence of independent variables on dependent variables and point forecasting. The models were also used for forecasting purposes. Statistical models were built on the basis of historical production results of selected seven Polish mines. On the basis of variability of geological and mining conditions at 120 longwalls, the influence of individual parameters on longwall mining between 2010 and 2019 was determined. The identified relationships made it possible to formulate numerical forecast of unit production cost and daily longwall mining output in relation to the level of expected onerousness. The projection period was assumed to be 2020–2030. On this basis, an opinion was formulated on the forecast of the expected unit production costs and the output of the 259 longwalls planned to be mined at these mines. A procedure scheme was developed using the following methods: 1) Analytic Hierarchy Process (AHP) – mathematical multi-criteria decision-making method, 2) comparative multivariate analysis, 3) regression analysis, 4) Monte Carlo simulation. The utilitarian purpose of the monograph is to provide the research community with the concept of building models that can be used to solve real decision-making problems during longwall planning in hard coal mines. The layout of the monograph, consisting of an introduction, eight main sections and a conclusion, follows the objectives set out above. Section One presents the methodology used to assess the impact of onerous geological and mining conditions on the mining process. Multi-Criteria Decision Analysis (MCDA) is reviewed and basic definitions used in the following part of the paper are introduced. The section includes a description of AHP which was used in the presented analysis. Individual factors resulting from natural hazards, from the geological structure of the deposit (seam), from limitations caused by technical requirements, from the impact of mining on the environment, which affect the mining process, are described exhaustively in Section Two. Sections Three and Four present the construction of two hierarchical models of geological and mining conditions onerousness: the first in the context of extraction costs and the second in relation to daily longwall mining. The procedure for valuing the importance of their components by a group of experts (pairwise comparison of criteria and sub-criteria on the basis of Saaty’s 9-point comparison scale) is presented. The AHP method is very sensitive to even small changes in the value of the comparison matrix. In order to determine the stability of the valuation of both onerousness models, a sensitivity analysis was carried out, which is described in detail in Section Five. Section Six is devoted to the issue of constructing aggregate indices, WUe and WUt, which synthetically measure the impact of onerous geological and mining conditions on the mining process in individual longwalls and allow for a linear ordering of longwalls according to increasing levels of onerousness. Section Seven opens the research part of the work, which analyses the results of the developed models and indicators in individual mines. A detailed analysis is presented of the assessment of the impact of onerous mining conditions on mining costs in selected seams of the analysed mines, and in the case of the impact of onerous mining on daily longwall mining output, the variability of this process in individual fields (lots) of the mines is characterised. Section Eight presents the regression equations for the dependence of the costs and level of extraction on the aggregated onerousness indicators, WUe and WUt. The regression models f(KJC_N) and f(W) developed in this way are used to forecast the unit mining costs and daily output of the designed longwalls in the context of diversified geological and mining conditions. The use of regression models is of great practical importance. It makes it possible to approximate unit costs and daily output for newly designed longwall workings. The use of this knowledge may significantly improve the quality of planning processes and the effectiveness of the mining process.

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Частини книг з теми "Approximate Mining"

Fiorentino, Nicola, Cristian Molinaro, and Irina Trubitsyna. "Approximate Query Answering over Incomplete Data." In Complex Pattern Mining, 213–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36617-9_13.

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Wang, Liang, Christopher Leckie, Kotagiri Ramamohanarao, and James Bezdek. "Approximate Spectral Clustering." In Advances in Knowledge Discovery and Data Mining, 134–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_15.

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Tripathy, B. K., Prateek Saraf, and S. Ch Parida. "On Multigranular Approximate Rough Equivalence of Sets and Approximate Reasoning." In Computational Intelligence in Data Mining - Volume 2, 605–16. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2208-8_55.

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Ferreira, Pedro G., Paulo J. Azevedo, Cândida G. Silva, and Rui M. M. Brito. "Mining Approximate Motifs in Time Series." In Discovery Science, 89–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893318_12.

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Soulet, Arnaud, and François Rioult. "Exact and Approximate Minimal Pattern Mining." In Advances in Knowledge Discovery and Management, 61–81. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45763-5_4.

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Aizaz, Zainab, Kavita Khare, and Aizaz Tirmizi. "Efficient Approximate Multipliers for Neural Network Applications." In Computational Intelligence in Data Mining, 577–89. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9447-9_44.

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Acosta-Mendoza, Niusvel, Andrés Gago-Alonso, and José E. Medina-Pagola. "On Speeding up Frequent Approximate Subgraph Mining." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 316–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33275-3_39.

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Ding, Lizhong, and Shizhong Liao. "Nyström Approximate Model Selection for LSSVM." In Advances in Knowledge Discovery and Data Mining, 282–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30217-6_24.

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Wang, Jingdong, Jing Wang, Qifa Ke, Gang Zeng, and Shipeng Li. "Fast Approximate $$K$$ K -Means via Cluster Closures." In Multimedia Data Mining and Analytics, 373–95. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_17.

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Zong, Yu, Guandong Xu, Ping Jin, Yanchun Zhang, EnHong Chen, and Rong Pan. "APPECT: An Approximate Backbone-Based Clustering Algorithm for Tags." In Advanced Data Mining and Applications, 175–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25853-4_14.

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Тези доповідей конференцій з теми "Approximate Mining"

Vilim, Matthew, Henry Duwe, and Rakesh Kumar. "Approximate bitcoin mining." In DAC '16: The 53rd Annual Design Automation Conference 2016. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2897937.2897988.

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Spyropoulou, Eirini, and Tijl De Bie. "Mining approximate multi-relational patterns." In 2014 International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2014. http://dx.doi.org/10.1109/dsaa.2014.7058115.

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Ning Pan, Zhiqiang Zhu, Liangsheng He, Lei Sun, and Hang Su. "Mining approximate roles under important assignment." In 2016 2nd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2016. http://dx.doi.org/10.1109/compcomm.2016.7924918.

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Li, Ruirui, and Wei Wang. "REAFUM: Representative Approximate Frequent Subgraph Mining." In Proceedings of the 2015 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974010.85.

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Kenig, Batya, Pranay Mundra, Guna Prasaad, Babak Salimi, and Dan Suciu. "Mining Approximate Acyclic Schemes from Relations." In SIGMOD/PODS '20: International Conference on Management of Data. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3318464.3380573.

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Silvestri, Claudio, and Salvatore Orlando. "Distributed approximate mining of frequent patterns." In the 2005 ACM symposium. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1066677.1066796.

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Seo, San Ha, and Saeed Salem. "Mining representative approximate frequent coexpression subnetworks." In BCB '20: 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3388440.3415584.

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Anchuri, Pranay, Mohammed J. Zaki, Omer Barkol, Shahar Golan, and Moshe Shamy. "Approximate graph mining with label costs." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2487575.2487602.

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Wang, Lijun, Ming Dong, and Alexander Kotov. "Multi-level Approximate Spectral Clustering." In 2015 IEEE International Conference on Data Mining (ICDM). IEEE, 2015. http://dx.doi.org/10.1109/icdm.2015.38.

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Li, Haifeng, Zongjian Lu, and Hong Chen. "Mining Approximate Closed Frequent Itemsets over Stream." In 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing. IEEE, 2008. http://dx.doi.org/10.1109/snpd.2008.32.

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Звіти організацій з теми "Approximate Mining"

Weller, Joel I., Derek M. Bickhart, Micha Ron, Eyal Seroussi, George Liu, and George R. Wiggans. Determination of actual polymorphisms responsible for economic trait variation in dairy cattle. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7600017.bard.

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Анотація:

The project’s general objectives were to determine specific polymorphisms at the DNA level responsible for observed quantitative trait loci (QTLs) and to estimate their effects, frequencies, and selection potential in the Holstein dairy cattle breed. The specific objectives were to (1) localize the causative polymorphisms to small chromosomal segments based on analysis of 52 U.S. Holstein bulls each with at least 100 sons with high-reliability genetic evaluations using the a posteriori granddaughter design; (2) sequence the complete genomes of at least 40 of those bulls to 20 coverage; (3) determine causative polymorphisms based on concordance between the bulls’ genotypes for specific polymorphisms and their status for a QTL; (4) validate putative quantitative trait variants by genotyping a sample of Israeli Holstein cows; and (5) perform gene expression analysis using statistical methodologies, including determination of signatures of selection, based on somatic cells of cows that are homozygous for contrasting quantitative trait variants; and (6) analyze genes with putative quantitative trait variants using data mining techniques. Current methods for genomic evaluation are based on population-wide linkage disequilibrium between markers and actual alleles that affect traits of interest. Those methods have approximately doubled the rate of genetic gain for most traits in the U.S. Holstein population. With determination of causative polymorphisms, increasing the accuracy of genomic evaluations should be possible by including those genotypes as fixed effects in the analysis models. Determination of causative polymorphisms should also yield useful information on gene function and genetic architecture of complex traits. Concordance between QTL genotype as determined by the a posteriori granddaughter design and marker genotype was determined for 30 trait-by-chromosomal segment effects that are segregating in the U.S. Holstein population; a probability of <10²⁰ was used to accept the null hypothesis that no segregating gene within the chromosomal segment was affecting the trait. Genotypes for 83 grandsires and 17,217 sons were determined by either complete sequence or imputation for 3,148,506 polymorphisms across the entire genome. Variant sites were identified from previous studies (such as the 1000 Bull Genomes Project) and from DNA sequencing of bulls unique to this project, which is one of the largest marker variant surveys conducted for the Holstein breed of cattle. Effects for stature on chromosome 11, daughter pregnancy rate on chromosome 18, and protein percentage on chromosome 20 met 3 criteria: (1) complete or nearly complete concordance, (2) nominal significance of the polymorphism effect after correction for all other polymorphisms, and (3) marker coefficient of determination >40% of total multiple-regression coefficient of determination for the 30 polymorphisms with highest concordance. The missense polymorphism Phe279Tyr in GHR at 31,909,478 base pairs on chromosome 20 was confirmed as the causative mutation for fat and protein concentration. For effect on fat percentage, 12 additional missensepolymorphisms on chromosome 14 were found that had nearly complete concordance with the suggested causative polymorphism (missense mutation Ala232Glu in DGAT1). The markers used in routine U.S. genomic evaluations were increased from 60,000 to 80,000 by adding markers for known QTLs and markers detected in BARD and other research projects. Objectives 1 and 2 were completely accomplished, and objective 3 was partially accomplished. Because no new clear-cut causative polymorphisms were discovered, objectives 4 through 6 were not completed.

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