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Relevant bibliographies by topics / Approximate Mining

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Dissertations / Theses

Academic literature on the topic 'Approximate Mining'

Author: Grafiati

Published: 11 March 2023

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Dissertations / Theses on the topic "Approximate Mining"

1

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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2

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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4

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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5

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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6

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 de

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7

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<br>東吳大學<br>資訊科學系<br>96<br>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,

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8

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,

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9

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.<br>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

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10

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

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