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Готові списки джерел за темами / Mining Techniques and Pattern Discovery

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Добірка наукової літератури з теми "Mining Techniques and Pattern Discovery"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Mining Techniques and Pattern Discovery"

1

J., Umarani, and S. Manikandan Dr. "PATTERN DISCOVERY TECHNIQUES IN WEB USAGE MINING." International Journal of Scientific Research and Modern Education 3, no. 2 (2018): 1–3. https://doi.org/10.5281/zenodo.1332044.

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Анотація:

WWW is a very popular and interactive medium for broadcasting information today. Due to the vast, diverse and lively nature of web it advancesthe scalability, multimedia data and temporal issues respectively. The development of the web has given rise to large quantity of data that is freely available for user access.Web Usage Mining enhances the user experience while browsing web pages by using past history of web data. It also used to improve the web site navigation. Web mining makes use of data mining techniques and deciphers potentially useful information from web data. Web usage mining is

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Suraj, Jain1 Siddu P. Algur2 *Basavaraj A. Goudannavar 3. Prashant Bhat4. "REVIEW ON WEB MULTIMEDIA MINING AND KNOWLEDGE DISCOVERY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 3 (2018): 449–55. https://doi.org/10.5281/zenodo.1199346.

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Анотація:

Web Multimedia data mining (WMDM) can be defined as the process of finding interesting patterns from media data such as audio, video, image and text that are not ordinarily accessible by basic queries and associated results. MDM is the mining of knowledge and high level multimedia information from large multimedia database system. MDM refers to pattern discovery, rule extraction and knowledge acquisition from multimedia database. To extract knowledge from multimedia database multimedia techniques are used. We compare MDM techniques with the state of the art data mining techniques involving clu

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3

Et. al., V. Aruna,. "A Review on Design and Development Of Sequential Patterns Algorithms In Web Usage Mining." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 1634–39. http://dx.doi.org/10.17762/turcomat.v12i2.1448.

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Анотація:

In the recent years with the advancement in technology, a lot of information is available in different formats and extracting the knowledge from that data has become a very difficult task. Due to the vast amount of information available on the web, users are finding it difficult to extract relevant information or create new knowledge using information available on the web. To solve this problem Web mining techniques are used to discover the interesting patterns from the hidden data .Web Usage Mining (WUM), which is one of the subset of Web Mining helps in extracting the hidden knowledge presen

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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 (2021): 8900. http://dx.doi.org/10.3390/su13168900.

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Анотація:

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 transform

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Xu, Dalin, and Yingmei Wei. "Study on Visual Techniques of Potential Pattern Discovery for Time Series Data." MATEC Web of Conferences 232 (2018): 02049. http://dx.doi.org/10.1051/matecconf/201823202049.

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Анотація:

Sequential pattern mining is always a very important branch of time series data mining. The pattern mining with visual means can be used to extract the knowledge of time series data more intuitively. Based on the research content, this paper analyzes the sequence pattern mining methods in different aspects and their combination with visualization technology. We further discuss and summarize the advantages of different visualization methods in discovering the potential patterns in time series data. Different systems and models have their unique information to show the focus. Compared with the c

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Muley, Abhinav. "Global Data Fusion versus Local Pattern Fusion in Mining Multiple Databases: A Comparative Review." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 3844–49. http://dx.doi.org/10.1166/jctn.2020.9046.

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Анотація:

With the emergence of big data, mining distributed databases has become a critical task in the domain of discovery of knowledge from databases. Many of the traditional multiple-database mining methods developed until now have emphasized mining the mono-database, which is a pool of all the local databases merged at a central site; local patterns discovered at local sites are not analyzed in mono-database mining. However, in real-world applications, data collected from multiple databases may be duplicitous and unreliable. Therefore, developing methods to discover reliable, high-quality knowledge

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Babu, S. Suresh, Vahiduddin Shariff, and CH M. H. Saibaba. "Data Mining Techniques Based on Effective Pattern Discovery." International Journal of u- and e- Service, Science and Technology 9, no. 7 (2016): 197–202. http://dx.doi.org/10.14257/ijunesst.2016.9.7.20.

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Patel, Ketul, and Dr A. R. Patel. "Process of Web Usage Mining to find Interesting Patterns from Web Usage Data." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (2012): 144–48. http://dx.doi.org/10.24297/ijct.v3i1c.2767.

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Анотація:

The traffic on World Wide Web is increasing rapidly and huge amount of data is generated due to usersâ€™ numerous interactions with web sites. Web Usage Mining is the application of data mining techniques to discover the useful and interesting patterns from web usage data. It supports to know frequently accessed pages, predict user navigation, improve web site structure etc. In order to apply Web Usage Mining, various steps are performed. This paper discusses the process of Web Usage Mining consisting steps: Data Collection, Pre-processing, Pattern Discovery and Pattern Analysis. It has also p

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Ouyang, Zhiping, Lizhen Wang, and Pingping Wu. "Spatial Co-Location Pattern Discovery from Fuzzy Objects." International Journal on Artificial Intelligence Tools 26, no. 02 (2017): 1750003. http://dx.doi.org/10.1142/s0218213017500038.

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Анотація:

A spatial co-location pattern is a group of spatial objects whose instances are frequently located in the same region. The spatial co-location pattern mining problem has been investigated extensively in the past due to its broad range of applications. In this paper we study this problem for fuzzy objects. Fuzzy objects play an important role in many areas, such as the geographical information system and the biomedical image database. In this paper, we propose two new kinds of co-location pattern mining for fuzzy objects, single co-location pattern mining (SCP) and range co-location pattern min

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ALI, Sura I. Mohammed, and Rafid Habib BUTI. "DATA MINING IN HEALTHCARE SECTOR." MINAR International Journal of Applied Sciences and Technology 03, no. 02 (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 t

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Дисертації з теми "Mining Techniques and Pattern Discovery"

1

Skabar, Andrew Alojz. "Inductive learning techniques for mineral potential mapping." Thesis, Queensland University of Technology, 2001.

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Cao, Huiping. "Pattern discovery from spatiotemporal data." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37381520.

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Cao, Huiping, and 曹會萍. "Pattern discovery from spatiotemporal data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37381520.

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Pipanmaekaporn, Luepol. "A data mining framework for relevance feature discovery." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/62857/1/Luepol_Pipanmaekaporn_Thesis.pdf.

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Анотація:

This thesis is a study for automatic discovery of text features for describing user information needs. It presents an innovative data-mining approach that discovers useful knowledge from both relevance and non-relevance feedback information. The proposed approach can largely reduce noises in discovered patterns and significantly improve the performance of text mining systems. This study provides a promising method for the study of Data Mining and Web Intelligence.

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Ke, Yiping. "Efficient correlated pattern discovery in databases /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20KE.

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Wu, Sheng-Tang. "Knowledge discovery using pattern taxonomy model in text mining." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16675/1/Sheng-Tang_Wu_Thesis.pdf.

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Анотація:

In the last decade, many data mining techniques have been proposed for fulfilling various knowledge discovery tasks in order to achieve the goal of retrieving useful information for users. Various types of patterns can then be generated using these techniques, such as sequential patterns, frequent itemsets, and closed and maximum patterns. However, how to effectively exploit the discovered patterns is still an open research issue, especially in the domain of text mining. Most of the text mining methods adopt the keyword-based approach to construct text representations which consist of single w

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Wu, Sheng-Tang. "Knowledge discovery using pattern taxonomy model in text mining." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16675/.

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Анотація:

In the last decade, many data mining techniques have been proposed for fulfilling various knowledge discovery tasks in order to achieve the goal of retrieving useful information for users. Various types of patterns can then be generated using these techniques, such as sequential patterns, frequent itemsets, and closed and maximum patterns. However, how to effectively exploit the discovered patterns is still an open research issue, especially in the domain of text mining. Most of the text mining methods adopt the keyword-based approach to construct text representations which consist of single w

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Preti, Giulia. "On the discovery of relevant structures in dynamic and heterogeneous data." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/242978.

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Анотація:

We are witnessing an explosion of available data coming from a huge amount of sources and domains, which is leading to the creation of datasets larger and larger, as well as richer and richer. Understanding, processing, and extracting useful information from those datasets requires specialized algorithms that take into consideration both the dynamism and the heterogeneity of the data they contain. Although several pattern mining techniques have been proposed in the literature, most of them fall short in providing interesting structures when the data can be interpreted differently from user t

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Preti, Giulia. "On the discovery of relevant structures in dynamic and heterogeneous data." Doctoral thesis, Università degli studi di Trento, 2019. http://hdl.handle.net/11572/242978.

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Анотація:

We are witnessing an explosion of available data coming from a huge amount of sources and domains, which is leading to the creation of datasets larger and larger, as well as richer and richer. Understanding, processing, and extracting useful information from those datasets requires specialized algorithms that take into consideration both the dynamism and the heterogeneity of the data they contain. Although several pattern mining techniques have been proposed in the literature, most of them fall short in providing interesting structures when the data can be interpreted differently from user to

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ZANONI, MARCO. "Data mining techniques for design pattern detection." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2012. http://hdl.handle.net/10281/31515.

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Анотація:

The main objective of design pattern detection is to gain better comprehension of a software system, and of the kind of problems addressed during the development of the system itself. Design patterns have informal specifications, leading to many implementation variants caused by the subjective interpretation of the pattern by developers. This thesis applies a supervised classification approach to make the detection more subjective, bringing to developers the patterns they want to find, ranked by a confidence value.

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Книги з теми "Mining Techniques and Pattern Discovery"

1

Yang, Jian. Intelligent Science and Intelligent Data Engineering: Third Sino-foreign-interchange Workshop, IScIDE 2012, Nanjing, China, October 15-17, 2012. Revised Selected Papers. Springer Berlin Heidelberg, 2013.

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2

Yadav, Vikash, Anil Kumar Dubey, Harivans Pratap Singh, Gaurav Dubey, and Erma Suryani. Process Mining Techniques for Pattern Recognition. CRC Press, 2022. http://dx.doi.org/10.1201/9781003169550.

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3

Pal, Nikhil R., and Lakhmi Jain, eds. Advanced Techniques in Knowledge Discovery and Data Mining. Springer London, 2005. http://dx.doi.org/10.1007/1-84628-183-0.

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4

R, Pal Nikhil, and Jain L. C, eds. Advanced techniques in knowledge discovery and data mining. Springer-Verlag, 2004.

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5

R, Pal Nikhil, and Jain L. C, eds. Advanced techniques in knowledge discovery and data mining. Springer-Verlag, 2005.

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6

L, Wang Jason T., Shapiro Bruce A, and Shasha Dennis Elliott, eds. Pattern discovery in biomolecular data: Tools, techniques, and applications. Oxford University, 1999.

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7

Kumar, Pradeep. Pattern discovery using sequence data mining: Applications and studies. Information Science Reference, 2012.

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8

Alam, Shafiq. Biologically-inspired techniques for knowledge discovery and data mining. Information Science Reference, an imprint of IGI Global, 2014.

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9

Atzmüller, Martin. Knowledge-intensive subgroup mining: Techniques for automatic and interactive discovery. Aka, Akademische Verlagsgsellschaft, 2007.

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10

May, Michael. Ubiquitous Knowledge Discovery: Challenges, Techniques, Applications. Springer Berlin Heidelberg, 2010.

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Частини книг з теми "Mining Techniques and Pattern Discovery"

1

Dhifli, Wajdi, and Engelbert Mephu Nguifo. "Motif Discovery in Protein 3D-Structures using Graph Mining Techniques." In Pattern Recognition in Computational Molecular Biology. John Wiley & Sons, Inc, 2015. http://dx.doi.org/10.1002/9781119078845.ch10.

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2

Zhang, Chao, and Jiawei Han. "Data Mining and Knowledge Discovery." In Urban Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_42.

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AbstractOur physical world is being projected into online cyberspace at an unprecedented rate. People nowadays visit different places and leave behind them million-scale digital traces such as tweets, check-ins, Yelp reviews, and Uber trajectories. Such digital data are a result of social sensing: namely people act as human sensors that probe different places in the physical world and share their activities online. The availability of massive social-sensing data provides a unique opportunity for understanding urban space in a data-driven manner and improving many urban computing applications,

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Ahmed, Chowdhury Farhan, Syed Khairuzzaman Tanbeer, Byeong-Soo Jeong, and Young-Koo Lee. "An Efficient Candidate Pruning Technique for High Utility Pattern Mining." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_76.

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4

Ma, Shuai, Shiwei Tang, Dongqing Yang, Tengjiao Wang, and Jinqiang Han. "Combining Clustering with Moving Sequential Pattern Mining: A Novel and Efficient Technique." In Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24775-3_51.

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5

Jacob, Shomona Gracia, R. Geetha Ramani, and P. Nancy. "Discovery of Knowledge Patterns in Lymphographic Clinical Data through Data Mining Methods and Techniques." In Advances in Computing and Information Technology. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31600-5_13.

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Ventura, Sebastián, and José María Luna. "Subgroup Discovery." In Supervised Descriptive Pattern Mining. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98140-6_4.

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7

Calders, Toon. "Recent Developments in Pattern Mining." In Discovery Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33492-4_2.

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van der Aalst, Wil. "Advanced Process Discovery Techniques." In Process Mining. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49851-4_7.

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van der Aalst, Wil M. P. "Advanced Process Discovery Techniques." In Process Mining. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19345-3_6.

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Ugarte, Willy, Patrice Boizumault, Samir Loudni, and Bruno Crémilleux. "Soft Threshold Constraints for Pattern Mining." In Discovery Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33492-4_25.

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Тези доповідей конференцій з теми "Mining Techniques and Pattern Discovery"

1

Biagiotti, Stephen F., Mark Madden, and Elaine S. Hendren. "Risk Mining: a Predictive Tool to Enhance Pipeline Integrity Assessment." In CORROSION 2002. NACE International, 2002. https://doi.org/10.5006/c2002-02073.

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Анотація:

Abstract Standard methods of evaluating pipeline integrity have stressed index-based and conditional based data assessment processes. Recent works, however, have emphasized the importance of predictive techniques using associations, correlations, sequential patterns and other relationships in evaluating pipeline integrity. Data mining represents a shift from verification-driven data analysis approaches to discovery-driven methods in integrity evaluation. Risk mining involves the analysis of large quantities of data in the process of discovering meaningful new correlations, patterns and trends

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2

M, Mahendra, Adarsha S. P, Anant Saraswat, and D. Nagaraju. "Art of Rare Pattern Discovery Techniques in Data Mining." In 2023 Global Conference on Information Technologies and Communications (GCITC). IEEE, 2023. http://dx.doi.org/10.1109/gcitc60406.2023.10426173.

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3

Calderón-Ruiz, Guillermo, and Marcos Sepúlveda. "Automatic discovery of failures in business processes using Process Mining techniques." In Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação, 2013. http://dx.doi.org/10.5753/sbsi.2013.5710.

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One of the most common and costly problems that organizations are facing is to find the causes of failures in business processes. Failures are often due to missing or unnecessary execution of some process activities; or with how some activities are performed. Currently, there is no automatic technique that helps finding these causes. We propose a novel technique to identify potential causes of failures in business process by extending available Process Mining techniques. Initially, the original event log is filtered in two logs, the former with successful cases and the latter with failed cases

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4

Tristão, Cristian, Duncan D. Ruiz, and Karin Becker. "FlowSpy: exploring Activity-Execution Patterns from Business Processes." In Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação, 2008. http://dx.doi.org/10.5753/sbsi.2008.5923.

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The paper describes FlowSpy, an environment that employs a sequence mining technique to discover and analyze actual process execution paths from business processes, for both process comparison and process discovery. FlowSpy focuses on exploratory analysis of the different execution flows, enabling a detailed analysis of business behavior, quantification of different execution flows, and abstraction mechanisms (log pre-processing and visualization abstraction) that deal with process complexity and different process views. Log pre-processing aims at improving the data mining phase, with a more r

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Campisano, Riccardo, Fabio Porto, Esther Pacitti, Florent Masseglia, and Eduardo Ogasawara. "Spatial Sequential Pattern Mining for Seismic Data." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2016. http://dx.doi.org/10.5753/sbbd.2016.24335.

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Анотація:

A myriad of applications from different domains collects time series data for further analysis. In many of them, such as seismic datasets, the observed data is also associated to a space dimension, which corresponds, in fact, to spatial-time series. The analysis of these datasets is difficult due to both the continuous nature of the observed data and the relationship between spatial and time dimensions. Meanwhile, sequential patterns mining techniques have been successfully used in large volume of transactional databases to obtain insights from data. In this work, we start exploring the discov

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Borges, Heraldo, Antonio Castro, Rafaelli Coutinho, Fabio Porto, Esther Pacitti, and Eduardo Ogasawara. "STMotif Explorer: A Tool for Spatiotemporal Motif Analysis." In Anais Estendidos do Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/sbbd_estendido.2023.233371.

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Анотація:

Pattern discovery is an important task in time series mining. A pattern that occurs a significant number of times in a time series is called a motif. Several approaches have been developed to discover motifs in time series. However, we can observe a clear gap in exploring the spatial-time series data. It is challenging to understand and characterize the meaning of the motif obtained concerning the data domain, comparing different approaches and analyzing the quality of the results obtained. We propose STMotif Explorer, a spatial-time motif analysis system that aims to interactively discover an

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"Extraction Student Dropout Patterns with Data Mining Techniques in Undergraduate Programs." In International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004543001360142.

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Shivaprasad, G., N. V. Subbareddy, U. Dinesh Acharya, R. B. Patel, and B. P. Singh. "Knowledge Discovery from Web Usage Data: Research and Development of Web Access Pattern Tree Based Sequential Pattern Mining Techniques: A Survey." In INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN SCIENCE AND TECHNOLOGY (ICM2ST-10). AIP, 2010. http://dx.doi.org/10.1063/1.3526223.

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Li, Chao-Wei, and Kuen-Fang Jea. "Using count prediction techniques for mining frequent patterns in transactional data streams." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6234217.

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Silva, Mariana O., Luiza de Melo-Gomes, and Mirella M. Moro. "Gender Representation in Literature: Analysis of Characters' Physical Descriptions." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/kdmile.2023.232571.

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This study employs Natural Language Processing (NLP) techniques to quantitatively analyze the descriptions of male and female body parts in Portuguese literature. We investigate these descriptions' frequency, specificity, and objectification by examining a corpus of literary works. The results indicate distinct differences in how male and female bodies are portrayed, revealing evidence of gender bias in the choice of specific descriptors for body parts. This research contributes to the ongoing discourse surrounding gender representation in literature, shedding light on the potential biases in

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Звіти організацій з теми "Mining Techniques and Pattern Discovery"

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Pou, Jose, Jeff Duffany, and Alfredo Cruz. Terrorist Activity Evaluation and Pattern Detection (TAE&PD) in Afghanistan: A Knowledge Discovery and Data Mining (KDDM) Approach for Counter-Terrorism. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada581564.

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Pasupuleti, Murali Krishna. Augmented Human Intelligence: Converging Generative AI, Quantum Computing, and XR for Enhanced Human-Machine Synergy. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv525.

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Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences.

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