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Journal articles on the topic 'Discovery from data'

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Author: Grafiati
Published: 10 March 2023

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1

Mak, H. Craig. "Discovery from data repositories." Nature Biotechnology 29, no. 1 (January 2011): 46–47. http://dx.doi.org/10.1038/nbt0111-46.

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2

Pazzani, M. J. "Knowledge discovery from data?" IEEE Intelligent Systems 15, no. 2 (March 2000): 10–12. http://dx.doi.org/10.1109/5254.850821.

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3

Gama, João, and Jesus Aguilar-Ruiz. "Knowledge discovery from data streams." Intelligent Data Analysis 11, no. 1 (March 15, 2007): 1–2. http://dx.doi.org/10.3233/ida-2007-11101.

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4

Gama, João, Jesus Aguilar-Ruiz, and Ralf Klinkenberg. "Knowledge discovery from data streams." Intelligent Data Analysis 12, no. 3 (May 30, 2008): 251–52. http://dx.doi.org/10.3233/ida-2008-12301.

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5

Gama, João, Auroop Ganguly, Olufemi Omitaomu, Raju Vatsavai, and Mohamed Gaber. "Knowledge discovery from data streams." Intelligent Data Analysis 13, no. 3 (May 27, 2009): 403–4. http://dx.doi.org/10.3233/ida-2009-0372.

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6

Morita, Chie, and Hiroshi Tsukimoto. "Knowledge discovery from numerical data." Knowledge-Based Systems 10, no. 7 (May 1998): 413–19. http://dx.doi.org/10.1016/s0950-7051(98)00040-9.

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7

Cook, Diane J., Lawrence B. Holder, and Surnjani Djoko. "Knowledge discovery from structural data." Journal of Intelligent Information Systems 5, no. 3 (November 1995): 229–48. http://dx.doi.org/10.1007/bf00962235.

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8

Kalenkova, Anna, Andrea Burattin, Massimiliano de Leoni, Wil van der Aalst, and Alessandro Sperduti. "Discovering high-level BPMN process models from event data." Business Process Management Journal 25, no. 5 (September 2, 2019): 995–1019. http://dx.doi.org/10.1108/bpmj-02-2018-0051.

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Purpose The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias. Design/methodology/approach The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of steps, performed to discover a model, containing various perspectives and presenting a holistic view of a process. This approach was implemented within an open-source process mining framework called ProM and proved its applicability for the analysis of real-life event logs. Findings This paper shows that the proposed integrated approach can be applied to real-life event logs of information systems from different domains. The multi-perspective process diagrams obtained within the approach are of good quality and better than models discovered using a technique that does not consider hierarchy. Moreover, due to the decomposition methods applied, the proposed approach can deal with large event logs, which cannot be handled by methods that do not use decomposition. Originality/value The paper consolidates various process mining techniques, which were never integrated before and presents a novel approach for the discovery of multi-perspective hierarchical BPMN models. This approach bridges the gap between well-known process mining techniques and a wide range of BPMN-complaint tools.
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9

Gottlob, Georg, and Pierre Senellart. "Schema mapping discovery from data instances." Journal of the ACM 57, no. 2 (January 2010): 1–37. http://dx.doi.org/10.1145/1667053.1667055.

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10

Vatsavai, Ranga Raju, Olufemi A. Omitaomu, Joao Gama, Nitesh V. Chawla, Mohamed Medhat Gaber, and Auroop R. Ganguly. "Knowledge discovery from sensor data (SensorKDD)." ACM SIGKDD Explorations Newsletter 10, no. 2 (December 20, 2008): 68–73. http://dx.doi.org/10.1145/1540276.1540297.

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11

Omitaomu, Olufemi A., Ranga Raju Vatsavai, Auroop R. Ganguly, Nitesh V. Chawla, Joao Gama, and Mohamed Medhat Gaber. "Knowledge discovery from sensor data (SensorKDD)." ACM SIGKDD Explorations Newsletter 11, no. 2 (May 27, 2010): 84–87. http://dx.doi.org/10.1145/1809400.1809417.

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12

Braun, Peter, Alfredo Cuzzocrea, Carson K. Leung, Adam G. M. Pazdor, and Kimberly Tran. "Knowledge Discovery from Social Graph Data." Procedia Computer Science 96 (2016): 682–91. http://dx.doi.org/10.1016/j.procs.2016.08.250.

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13

Zhu, Xiaofeng, Jie Shao, and Jilian Zhang. "Pattern discovery from multi-source data." Pattern Recognition Letters 109 (July 2018): 1–3. http://dx.doi.org/10.1016/j.patrec.2018.03.012.

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14

Chandola, Varun, Olufemi A. Omitaomu, Auroop R. Ganguly, Ranga R. Vatsavai, Nitesh V. Chawla, Joao Gama, and Mohamed M. Gaber. "Knowledge discovery from sensor data (SensorKDD)." ACM SIGKDD Explorations Newsletter 12, no. 2 (March 31, 2011): 50–53. http://dx.doi.org/10.1145/1964897.1964911.

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15

Im, Seunghyun, Zbigniew Raś, and Hanna Wasyluk. "Action rule discovery from incomplete data." Knowledge and Information Systems 25, no. 1 (July 1, 2009): 21–33. http://dx.doi.org/10.1007/s10115-009-0221-3.

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16

Yang, Yongliang, S. James Adelstein, and Amin I. Kassis. "Target discovery from data mining approaches." Drug Discovery Today 14, no. 3-4 (February 2009): 147–54. http://dx.doi.org/10.1016/j.drudis.2008.12.005.

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17

Yang, Yongliang, S. James Adelstein, and Amin I. Kassis. "Target discovery from data mining approaches." Drug Discovery Today 17 (February 2012): S16—S23. http://dx.doi.org/10.1016/j.drudis.2011.12.006.

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18

OTSUKI, Akira, and Masayoshi KAWAMURA. "Knowledge Discovery from Cadastral Information Big Data." Joho Chishiki Gakkaishi 23, no. 2 (2013): 327–32. http://dx.doi.org/10.2964/jsik.23_327.

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19

Holder, L. B., and D. J. Cook. "Discovery of inexact concepts from structural data." IEEE Transactions on Knowledge and Data Engineering 5, no. 6 (1993): 992–94. http://dx.doi.org/10.1109/69.250085.

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20

YAMANISHI, Kenji. "Discovery of Deep Knowledge from Complex Data." Journal of the Society of Mechanical Engineers 118, no. 1163 (2015): 616–19. http://dx.doi.org/10.1299/jsmemag.118.1163_616.

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21

Ao Kong, Chinmaya Gupta, Mauro Ferrari, Marco Agostini, Chiara Bedin, Ali Bouamrani, Ennio Tasciotti, and Robert Azencott. "Biomarker Signature Discovery from Mass Spectrometry Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 11, no. 4 (July 1, 2014): 766–72. http://dx.doi.org/10.1109/tcbb.2014.2318718.

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22

Afify, Ashraf A. "Discovery of association rules from manufacturing data." International Journal of Computer Aided Engineering and Technology 3, no. 3/4 (2011): 360. http://dx.doi.org/10.1504/ijcaet.2011.040053.

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23

Cholleti, Sharath R., Sanjay Agravat, Tim Morris, Joel H. Saltz, Xuezheng Song, Richard D. Cummings, and David F. Smith. "Automated Motif Discovery from Glycan Array Data." OMICS: A Journal of Integrative Biology 16, no. 10 (October 2012): 497–512. http://dx.doi.org/10.1089/omi.2012.0013.

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24

Rani, K. Swarupa. "Tree Representation: Knowledge Discovery from Uncertain Data." Procedia Computer Science 78 (2016): 683–90. http://dx.doi.org/10.1016/j.procs.2016.02.117.

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25

Shettar, Rajashree. "Sequential Pattern Discovery from Web Log Data." International Journal of Computer Applications 42, no. 8 (March 31, 2012): 8–11. http://dx.doi.org/10.5120/5710-7766.

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26

Baurley, James W., David V. Conti, W. James Gauderman, and Duncan C. Thomas. "Discovery of complex pathways from observational data." Statistics in Medicine 29, no. 19 (June 15, 2010): 1998–2011. http://dx.doi.org/10.1002/sim.3962.

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27

Van Hulse, Jason, and Taghi Khoshgoftaar. "Knowledge discovery from imbalanced and noisy data." Data & Knowledge Engineering 68, no. 12 (December 2009): 1513–42. http://dx.doi.org/10.1016/j.datak.2009.08.005.

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28

Liu, Jixue, Feiyue Ye, Jiuyong Li, and Junhu Wang. "On discovery of functional dependencies from data." Data & Knowledge Engineering 86 (July 2013): 146–59. http://dx.doi.org/10.1016/j.datak.2013.01.008.

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29

Mahoto, Naeem Ahmed, Asadullah Shaikh, Mana Saleh Al Reshan, Muhammad Ali Memon, and Adel Sulaiman. "Knowledge Discovery from Healthcare Electronic Records for Sustainable Environment." Sustainability 13, no. 16 (August 9, 2021): 8900. http://dx.doi.org/10.3390/su13168900.

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The medical history of a patient is an essential piece of information in healthcare agencies, which keep records of patients. Due to the fact that each person may have different medical complications, healthcare data remain sparse, high-dimensional and possibly inconsistent. The knowledge discovery from such data is not easily manageable for patient behaviors. It becomes a challenge for both physicians and healthcare agencies to discover knowledge from many healthcare electronic records. Data mining, as evidenced from the existing published literature, has proven its effectiveness in transforming large data collections into meaningful information and knowledge. This paper proposes an overview of the data mining techniques used for knowledge discovery in medical records. Furthermore, based on real healthcare data, this paper also demonstrates a case study of discovering knowledge with the help of three data mining techniques: (1) association analysis; (2) sequential pattern mining; (3) clustering. Particularly, association analysis is used to extract frequent correlations among examinations done by patients with a specific disease, sequential pattern mining allows extracting frequent patterns of medical events and clustering is used to find groups of similar patients. The discovered knowledge may enrich healthcare guidelines, improve their processes and detect anomalous patients’ behavior with respect to the medical guidelines.
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30

Wu, Wanqing, and Wenyu Mao. "An Efficient and Scalable Algorithm to Mine Functional Dependencies from Distributed Big Data." Sensors 22, no. 10 (May 19, 2022): 3856. http://dx.doi.org/10.3390/s22103856.

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A crucial step in improving data quality is to discover semantic relationships between data. Functional dependencies are rules that describe semantic relationships between data in relational databases and have been applied to improve data quality recently. However, traditional functional discovery algorithms applied to distributed data may lead to errors and the inability to scale to large-scale data. To solve the above problems, we propose a novel distributed functional dependency discovery algorithm based on Apache Spark, which can effectively discover functional dependencies in large-scale data. The basic idea is to use data redistribution to discover functional dependencies in parallel on multiple nodes. In this algorithm, we take a sampling approach to quickly remove invalid functional dependencies and propose a greedy-based task assignment strategy to balance the load. In addition, the prefix tree is used to store intermediate computation results during the validation process to avoid repeated computation of equivalence classes. Experimental results on real and synthetic datasets show that the proposed algorithm in this paper is more efficient than existing methods while ensuring accuracy.
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31

Wong, A. K. C., and Yang Wang. "High-order pattern discovery from discrete-valued data." IEEE Transactions on Knowledge and Data Engineering 9, no. 6 (1997): 877–93. http://dx.doi.org/10.1109/69.649314.

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32

Chen, Min, Anne Trefethen, Rene Banares-Alcantara, Marina Jirotka, Bob Coecke, Thomas Ertl, and Albrecht Schmidt. "From Data Analysis and Visualization to Causality Discovery." Computer 44, no. 10 (October 2011): 84–87. http://dx.doi.org/10.1109/mc.2011.313.

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33

Tanaka, Isao. "Data-Driven Materials Discovery from Large Chemistry Spaces." Matter 3, no. 2 (August 2020): 327–28. http://dx.doi.org/10.1016/j.matt.2020.07.010.

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34

Amaro, Rommie E. "Drug Discovery Gets a Boost from Data Science." Structure 24, no. 8 (August 2016): 1225–26. http://dx.doi.org/10.1016/j.str.2016.07.003.

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35

Hońko, Piotr. "Association discovery from relational data via granular computing." Information Sciences 234 (June 2013): 136–49. http://dx.doi.org/10.1016/j.ins.2013.01.004.

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36

Hand, David J. "Knowledge Discovery from Data Streams by João Gama." International Statistical Review 80, no. 1 (April 2012): 181–82. http://dx.doi.org/10.1111/j.1751-5823.2012.00179_5.x.

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37

Shein, Thi Thi, Sutheera Puntheeranurak, and Makoto Imamura. "Discovery of evolving companion from trajectory data streams." Knowledge and Information Systems 62, no. 9 (May 7, 2020): 3509–33. http://dx.doi.org/10.1007/s10115-020-01471-2.

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38

HOSAKA, Keisuke. "F011003 Big Data Mining : Knowledge discovery from huge,complicated data sets." Proceedings of Mechanical Engineering Congress, Japan 2012 (2012): _F011003–1—_F011003–5. http://dx.doi.org/10.1299/jsmemecj.2012._f011003-1.

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39

Hu, Chunchun, and Si Chen. "Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data." ISPRS International Journal of Geo-Information 10, no. 11 (November 17, 2021): 787. http://dx.doi.org/10.3390/ijgi10110787.

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The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles or animals; these studies either set too many limitations in the shape of the cluster and time continuity or only focus on the characteristic of the gathering. Meanwhile, little attention has been paid to the equidirectional movement of the aggregated objects and their loose coherence moving. In this study, we propose the concept of loosely moving congestion patterns that represent a group of moving objects together with similar movement tendency and loose coherence moving, which exhibit a potential congestion characteristic. Meanwhile, we also develop an accelerated algorithm called parallel equidirectional cluster-recombinant (PDCLUR) that runs on graphics processing units (GPUs) to detect congestion patterns from large-scale raw taxi-trajectory data. The case study results demonstrate the performance of our approach and its applicability to large trajectory dataset, and we can discover some significant loosely moving congesting patterns and when and where the most congested road segments are observed. The developed algorithm PDCLUR performs satisfactorily, affording an acceleration ratio of over 65 relative to the traditional sequential algorithms.
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40

Abdulkadium, Ahmed Mahdi, Raid Abd Alreda Shekan, and Haitham Ali Hussain. "Application of Data Mining and Knowledge Discovery in Medical Databases." Webology 19, no. 1 (January 20, 2022): 4912–24. http://dx.doi.org/10.14704/web/v19i1/web19329.

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While technical improvements in the form of computer-based healthcare information applications as well as hardware are enabling collecting of and access to healthcare data wieldier. In this context, there are tools to analyse and examine this medical data once it has been acquired and saved. Analysis of documented medical data records may help in the identification of hidden features and patterns that could significantly increase our understanding of disease onset and treatment therapies. Significantly, the progress in information and communications technologies (ICT) has outpaced our capacity to assess summarise, and extract insight from the data. Today, database management system has equipped us with the fundamental tools for the effective storage as well as lookup of massive data sets, but the topic of how to allow human beings to interpret and analyse huge data remains a challenging and unsolved challenge. So, sophisticated methods for automated data mining and knowledge discovery are required to deal with large data. In this study, an effort was made employing machine learning approach to acquire knowledge that will aid various personnel in taking decisions that will guarantee that the sustainability objectives on Health is achieved. Finally, the present data mining methodologies with data mining methods and also its deployment tools that are more helpful for healthcare services are addressed in depth.
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41

Lauber, Chris, and Stefan Seitz. "Opportunities and Challenges of Data-Driven Virus Discovery." Biomolecules 12, no. 8 (August 4, 2022): 1073. http://dx.doi.org/10.3390/biom12081073.

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Virus discovery has been fueled by new technologies ever since the first viruses were discovered at the end of the 19th century. Starting with mechanical devices that provided evidence for virus presence in sick hosts, virus discovery gradually transitioned into a sequence-based scientific discipline, which, nowadays, can characterize virus identity and explore viral diversity at an unprecedented resolution and depth. Sequencing technologies are now being used routinely and at ever-increasing scales, producing an avalanche of novel viral sequences found in a multitude of organisms and environments. In this perspective article, we argue that virus discovery has started to undergo another transformation prompted by the emergence of new approaches that are sequence data-centered and primarily computational, setting them apart from previous technology-driven innovations. The data-driven virus discovery approach is largely uncoupled from the collection and processing of biological samples, and exploits the availability of massive amounts of publicly and freely accessible data from sequencing archives. We discuss open challenges to be solved in order to unlock the full potential of data-driven virus discovery, and we highlight the benefits it can bring to classical (mostly molecular) virology and molecular biology in general.
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42

Han, Henry, and Xiaoqian Jiang. "Disease Biomarker Query from RNA-Seq Data." Cancer Informatics 13s1 (January 2014): CIN.S13876. http://dx.doi.org/10.4137/cin.s13876.

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As a revolutionary way to unveil transcription, RNA-Seq technologies are challenging bioinformatics for its large data volumes and complexities. A large number of computational models have been proposed for differential expression (DE) analysis and normalization from different standing points. However, there were no studies available yet to conduct disease biomarker discovery for this type of high-resolution digital gene expression data, which will actually be essential to explore its potential in clinical bioinformatics. Although there were many biomarker discovery algorithms available in traditional omics communities, they cannot be applied to RNA-Seq count data to seek biomarkers directly for its special characteristics. In this work, we have presented a biomarker discovery algorithm, SEQ-Marker for RNA-Seq data, which is built on a novel data-driven feature selection algorithm, nonnegative singular value approximation (NSVA), which contributes to the robustness and sensitivity of the following DE analysis by taking advantages of the built-in characteristics of RNA-Seq count data. As a biomarker discovery algorithm built on network marker topology, the proposed SEQ-Marker not only bridges transcriptomics and systems biology but also contributes to clinical diagnostics.
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43

Ding, Jun, Haiyan Hu, and Xiaoman Li. "SIOMICS: a novel approach for systematic identification of motifs in ChIP-seq data." Nucleic Acids Research 42, no. 5 (December 9, 2013): e35-e35. http://dx.doi.org/10.1093/nar/gkt1288.

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Abstract The identification of transcription factor binding motifs is important for the study of gene transcriptional regulation. The chromatin immunoprecipitation (ChIP), followed by massive parallel sequencing (ChIP-seq) experiments, provides an unprecedented opportunity to discover binding motifs. Computational methods have been developed to identify motifs from ChIP-seq data, while at the same time encountering several problems. For example, existing methods are often not scalable to the large number of sequences obtained from ChIP-seq peak regions. Some methods heavily rely on well-annotated motifs even though the number of known motifs is limited. To simplify the problem, de novo motif discovery methods often neglect underrepresented motifs in ChIP-seq peak regions. To address these issues, we developed a novel approach called SIOMICS to de novo discover motifs from ChIP-seq data. Tested on 13 ChIP-seq data sets, SIOMICS identified motifs of many known and new cofactors. Tested on 13 simulated random data sets, SIOMICS discovered no motif in any data set. Compared with two recently developed methods for motif discovery, SIOMICS shows advantages in terms of speed, the number of known cofactor motifs predicted in experimental data sets and the number of false motifs predicted in random data sets. The SIOMICS software is freely available at http://eecs.ucf.edu/∼xiaoman/SIOMICS/SIOMICS.html.
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44

Huang, Ying, Liyun Zhong, and Yan Chen. "Filtering Infrequent Behavior in Business Process Discovery by Using the Minimum Expectation." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 2 (April 2020): 1–15. http://dx.doi.org/10.4018/ijcini.2020040101.

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The aim of process discovery is to discover process models from the process execution data stored in event logs. In the era of “Big Data,” one of the key challenges is to analyze the large amounts of collected data in meaningful and scalable ways. Most process discovery algorithms assume that all the data in an event log fully comply with the process execution specification, and the process event logs are no exception. However, real event logs contain large amounts of noise and data from irrelevant infrequent behavior. The infrequent behavior or noise has a negative influence on the process discovery procedure. This article presents a technique to remove infrequent behavior from event logs by calculating the minimum expectation of the process event log. The method was evaluated in detail, and the results showed that its application in existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.
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45

Zhu, Tingting, Yezheng Liu, Jianshan Sun, and Chunhua Sun. "Topic discovery from short reviews based on data enhancement." Intelligent Data Analysis 26, no. 2 (March 14, 2022): 295–310. http://dx.doi.org/10.3233/ida-205715.

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With the rapid development of social media and mobile Internet, short reviews, such as Weibo and Twitter, have exploded online. Discovering topics from short reviews is significant for many practical applications. It can effectively not only identify users’ attitudes and emotions but also enhance customer satisfaction and shopping experience. Because reviews are relatively short, the sparsity of reviews considerably restricts the quality of topic discovery. To improve the efficiency of topic discovery, we introduce the concept of data enhancement and strengthen the data in sentences and words in short reviews based on the weight of importance. We then propose a topic model for reviews to topic discovery based on data enhancement (shorted as DE-LDA). We verify the rationality and feasibility of DE-LDA on real datasets. Results show that the proposed method outperforms benchmarks in topic discovery and also has better clustering effects.
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46

ALI, Sura I. Mohammed, and Rafid Habib BUTI. "DATA MINING IN HEALTHCARE SECTOR." MINAR International Journal of Applied Sciences and Technology 03, no. 02 (June 1, 2021): 87–91. http://dx.doi.org/10.47832/2717-8234.2-3.11.

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Disease detection is one of the applications where data mining techniques achieved more accurate and useful results. The healthcare sector collects massive volumes of healthcare data that are not mine to discover hidden data for better decision-making, a field of data mining introduces more efficiently and effectively to predict different kinds of diseases. Clustering medical data into small, meaningful chunks will help in pattern discovery by allowing for the retrieval of a large number of specific data points. The difference in using clustering the medical data from traditional data mining techniques is in extracting many features of the dataset that have been split into small segments to enable us to discover patterns by adding the data structure. By using clustering techniques, discovered overall correlations between data attributes. Selected data processing makes the mining process more efficient. The processed disease data are clustered using the K-means algorithm with the K values. Its ease of use and speed, which enable it to perform on a massive dataset. This paper highlights the theoretical side in using the K-Means Clustering algorithm in the context of data mining of disease detection and allowing for reliable and effective diagnosis.
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47

Donovan, Graham, and Qing Su. "Equation discovery from data: promise and pitfalls, from rabbits to Mars." New Zealand Journal of Mathematics 53 (October 12, 2022): 27–49. http://dx.doi.org/10.53733/216.

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The problem of equation discovery seeks to reconstruct the underlying dynamics of a time-varying system from observations of the system, and moreover to do so in an instructive way such that we may understand these underlying dynamics from the reconstruction.This article illustrates one type of modern equation discovery method (sparse identification of nonlinear dynamics, or SINDy) in the context of two classic problems. The presentation is in a tutorial style intended to be accessible to students, and could form a useful module in undergraduate or graduate courses in modelling, data analysis, or numerical methods. In this style we explore the strengths and limitations of these methods. We also demonstrate, through use of a carefully constructed example, a new result about the relationship between the reconstructed and true models when a na\"ive polynomial basis is used.
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48

Jiang, Yongyao, Yun Li, Chaowei Yang, Edward Armstrong, Thomas Huang, and David Moroni. "Reconstructing Sessions from Data Discovery and Access Logs to Build a Semantic Knowledge Base for Improving Data Discovery." ISPRS International Journal of Geo-Information 5, no. 5 (April 25, 2016): 54. http://dx.doi.org/10.3390/ijgi5050054.

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49

Doreswamy and K. S. Hemanth. "Hybrid Data Mining Technique for Knowledge Discovery from Engineering Materials Data Sets." International Journal of Database Management Systems 3, no. 1 (February 28, 2011): 166–77. http://dx.doi.org/10.5121/ijdms.2011.3111.

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50

Gullo, Francesco. "From Patterns in Data to Knowledge Discovery: What Data Mining Can Do." Physics Procedia 62 (2015): 18–22. http://dx.doi.org/10.1016/j.phpro.2015.02.005.

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