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Journal articles on the topic 'Multimedia Data Mining'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 June 2025

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1

Djeraba, Chabane. "Data mining from multimedia." International Journal of Parallel, Emergent and Distributed Systems 22, no. 6 (2007): 405–6. http://dx.doi.org/10.1080/17445760701207561.

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Han, Wei. "Study on Knowledge Services Based on Multi-Media Data Mining." Advanced Materials Research 774-776 (September 2013): 1794–97. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1794.

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There are a lot of data in the real life showed by the form of multimedia data. But the vast majority of data mining tools are developed for the relational database. Therefore it is necessary to introduce the service in multimedia data mining and multimedia data mining technology to improve service quality and level of knowledge. Multimedia data mining and knowledge services are introduced and multimedia data mining process is given. Meanwhile, multimedia data mining system prototype framework is proposed. In the end multimedia data mining application in the knowledge services is discussed.

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Bhatt, Chidansh, and Mohan Kankanhalli. "Probabilistic temporal multimedia data mining." ACM Transactions on Intelligent Systems and Technology 2, no. 2 (2011): 1–19. http://dx.doi.org/10.1145/1899412.1899421.

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4

Petrushin,, Valery A. "Multimedia Data Mining and Knowledge Discovery." Journal of Electronic Imaging 17, no. 4 (2007): 049901. http://dx.doi.org/10.1117/1.3040688.

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5

Paul, Prantosh K., and K. S. Shivraj. "Multimedia Data Mining and its Integration in Information Sector and Foundation: An Overview." Asian Journal of Computer Science and Technology 3, no. 1 (2014): 24–28. http://dx.doi.org/10.51983/ajcst-2014.3.1.1729.

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Information and Communication Technologies are one of the important component and toll. Virtually, the advent of Electronic resources and similar foundation use in Information Foundation and similar foundation has brought about significant changes in storage and communication of information. Data mining process consist of several process and stages, which are related to each other and interactive. This is the way of mining or extraction of data from the Database or Dataset. Extraction of Data with multimedia nature such as audio, video, images, text may be called as Multimedia Data Mining. In Information Foundation, Data Mining has wonderful role and importance. This paper is talks about Multimedia Information and Data Mining and its characteristics. Paper also talks about role and need of Multimedia Data Mining in Information and Similar Foundation.

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Bhoyar, Sanjay, Punam Bhoyar, Anuj Kumar, and Prabha Kiran. "Enhancing applications of surveillance through multimedia data mining." Journal of Discrete Mathematical Sciences and Cryptography 27, no. 3 (2024): 1105–20. http://dx.doi.org/10.47974/jdmsc-1947.

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Over recent years, multimedia data has become a cornerstone for insightful data analysis, yielding vital information crucial for informed decision-making processes. This diverse data format encompasses audio, video, images, and text, offering a wealth of valuable knowledge. Advancements in multimedia acquisition, storage, and processing technologies have significantly enhanced analytical capabilities, overcoming challenges posed by semi-structured and unstructured data formats. Various entities including corporations, governmental bodies, and academic institutions are keenly interested in harnessing insights from the vast reservoirs of multimedia data generated across diverse sources. Consequently, researchers have delved into data mining methodologies, uncovering effective strategies for extracting insights from multimedia datasets. This study aims to probe the conceptual and practical dimensions of multimedia data mining within surveillance contexts, elucidating its transformative impact on diverse sectors by facilitating efficient data collection, analysis, and dissemination processes. Moreover, it underscores the significance of incorporating relevant cryptography methods to bolster the system’s integrity and completeness.

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Yu, Chen, Yiwen Zhong, Thomas Smith, Ikhyun Park, and Weixia Huang. "Visual Data Mining of Multimedia Data for Social and Behavioral Studies." Information Visualization 8, no. 1 (2009): 56–70. http://dx.doi.org/10.1057/ivs.2008.32.

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With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, and so on) has been collected in research laboratories in various scientific disciplines, particularly in cognitive and behavioral studies. How to automatically and effectively discover new knowledge from rich multimedia data poses a compelling challenge because most state-of-the-art data mining techniques can only search and extract pre-defined patterns or knowledge from complex heterogeneous data. In light of this challenge, we propose a hybrid approach that allows scientists to use data mining as a first pass, and then forms a closed loop of visual analysis of current results followed by more data mining work inspired by visualization, the results of which can be in turn visualized and lead to the next round of visual exploration and analysis. In this way, new insights and hypotheses gleaned from the raw data and the current level of analysis can contribute to further analysis. As a first step toward this goal, we implement a visualization system with three critical components: (1) a smooth interface between visualization and data mining; (2) a flexible tool to explore and query temporal data derived from raw multimedia data; and (3) a seamless interface between raw multimedia data and derived data. We have developed various ways to visualize both temporal correlations and statistics of multiple derived variables as well as conditional and high-order statistics. Our visualization tool allows users to explore, compare and analyze multi-stream derived variables and simultaneously switch to access raw multimedia data.

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Yan, Yilin, Mei-Ling Shyu, and Qiusha Zhu. "Supporting Semantic Concept Retrieval with Negative Correlations in a Multimedia Big Data Mining System." International Journal of Semantic Computing 10, no. 02 (2016): 247–67. http://dx.doi.org/10.1142/s1793351x16400092.

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With the extensive use of smart devices and blooming popularity of social media websites such as Flickr, YouTube, Twitter, and Facebook, we have witnessed an explosion of multimedia data. The amount of data nowadays is formidable without effective big data technologies. It is well-acknowledged that multimedia high-level semantic concept mining and retrieval has become an important research topic; while the semantic gap (i.e., the gap between the low-level features and high-level concepts) makes it even more challenging. To address these challenges, it requires the joint research efforts from both big data mining and multimedia areas. In particular, the correlations among the classes can provide important context cues to help bridge the semantic gap. However, correlation discovery is computationally expensive due to the huge amount of data. In this paper, a novel multimedia big data mining system based on the MapReduce framework is proposed to discover negative correlations for semantic concept mining and retrieval. Furthermore, the proposed multimedia big data mining system consists of a big data processing platform with Mesos for efficient resource management and with Cassandra for handling data across multiple data centers. Experimental results on the TRECVID benchmark datasets demonstrate the feasibility and the effectiveness of the proposed multimedia big data mining system with negative correlation discovery for semantic concept mining and retrieval.

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9

THURAISINGHAM, BHAVANI. "MANAGING AND MINING MULTIMEDIA DATABASES." International Journal on Artificial Intelligence Tools 13, no. 03 (2004): 739–59. http://dx.doi.org/10.1142/s0218213004001776.

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Several advances have been made on managing multimedia databases as well as on data mining. Recently there is active research on mining multimedia databases. This paper provides an overview of managing multimedia databases and then describes issues on mining multimedia databases. In particular mining text, image, audio and video data are discussed.

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10

Mitra, Sushmita. "Data Mining: Multimedia, Soft Computing, and Bioinformatics." Journal of Electronic Imaging 15, no. 1 (2006): 019901. http://dx.doi.org/10.1117/1.2179076.

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11

Mulekar, Madhuri S. "Data Mining: Multimedia, Soft Computing, and Bioinformatics." Technometrics 46, no. 3 (2004): 368–69. http://dx.doi.org/10.1198/tech.2004.s207.

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12

Petrushin, Valery A., Jia-Yu (Tim) Pan, and Cees G. M. Snoek. "Tenth international workshop on multimedia data mining." ACM SIGKDD Explorations Newsletter 12, no. 2 (2011): 44–46. http://dx.doi.org/10.1145/1964897.1964909.

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13

Cheng, Yong Hong, Li Hua Ouyang, and Xin Yan Liu. "Research on the Mining Technology of Multimedia Communication Data Flow Based on Computer Network." Applied Mechanics and Materials 380-384 (August 2013): 3570–74. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3570.

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computer network multimedia communication has spread all over daily work and life fields, people also have put forward higher requirements for computer multimedia communication network. Based on current network multimedia communication, there are a large amount of data transmission, high-speed and dynamic characteristics, the mining technology of its communication data flow is carried out related to research. In this paper, the frequent item sets of data stream mining technology is carried out related to research, and the classical HCOUNT algorithm is carried out relevant analysis, according to the relevant analysis, the classical HCOUNT algorithm is improved, in order to alleviate possible error problem in data mining. Finally, the data stream mining algorithms are carried out relevant analysis of actual experimental data. Study on the technical aspects of the data flow mining in computer network multimedia communication, it is an important role in the development of the computer network multimedia communication. Study on the improvement of the current algorithm, it provides reference in the similar field of algorithm research

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Oswal, Prateek, and Divakar Singh. "Survey paper on various mining methods on multimedia Images." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 8, no. 3 (2013): 898–901. http://dx.doi.org/10.24297/ijct.v8i3.3400.

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Multimedia mining is a young but challenging subfield in data mining .Multimedia explanation represents an application of computer vision that presents the recognition of objects or ideas related to a multimedia document as a image. There is not unified conclusion in the concept, content and methods of Multimedia mining, Multimedia mining architecture and framework has to be further studied. there are various mining methods that we can apply on multimedia images like association rule mining, sequence mining, sequence pattern mining etc. In this survey paper we are focusing all this methods. We also discussed feature selection methods of various images.

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15

Abimbola Owonipa, Ayodeji, Taye Oladele Aro, and Oyenike Adunni Olukiran. "Multimedia Data Mining and Processing for News Source Attribution." International Journal of Research and Review 11, no. 5 (2024): 48–59. http://dx.doi.org/10.52403/ijrr.20240507.

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The desire for unbiased journalism that effectively counters disinformation is widely recognised. News consumers are not only interested in news, but they also want unbiased journalism that cuts through disinformation, and they want it from trusted news sources. Consequently, media researchers need to explore ways to facilitate news-source identification, irrespective of the platform used. However, the availability of multimedia data sources has seen a remarkable surge in recent years, encompassing demographic data, social media data, geodata, and pervasive digital trace data. Multimedia data mining is a procedure of discovering stimulating trends via media data using video, text, and audio that are not generally available by simple enquiries and related outputs. Researchers face the challenge of integrating these diverse sources to enhance news source attribution in multimedia data including platforms like Facebook, WhatsApp and Instagram. The paper presents a review of multimedia data approaches and their application to news source attribution research. Also, the examination of the benefits and limitations of these techniques and discussion on future directions were mentioned. Consideration was on machine learning and statistical approaches to multimedia data, which include deep learning, and probabilistic modelling. Similarly, a discussion on the importance of data privacy and ethics in news source attribution research was stated. The contribution of this study is highly relevant for news media research groups striving to improve their capability to attribute sources in multimedia data, thereby combatting disinformation and amplifying trusted media brands. Keywords: Data mining, Multimedia, Data process, News source attribution, Unbiased journalism

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16

Zeng, Zhihao. "Multimedia Computer-Aided Industrial System Design Based on the Study of Big Data Mining Algorithm." Advances in Multimedia 2021 (December 9, 2021): 1–8. http://dx.doi.org/10.1155/2021/8489662.

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Aiming at the problems of the multimedia computer-aided industrial system, this paper puts forward the application of big data mining algorithm to multimedia computer-aided industrial system design and analyzes in detail the impact of multimedia technology on industrial quality. This paper introduces the advantages of using big data mining algorithm in multimedia computer technology course, shows the operating environment to be met by using the multimedia computer-aided industrial system, follows the guiding principles of the overall design learning theory and artistic conception cognition theory, supplements specific industrial examples, and discusses multimedia industrial design.

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17

Li, Yang. "Research on Mining Technology of Communication Data Stream Based on Computer Network Multimedia." Applied Mechanics and Materials 427-429 (September 2013): 2094–98. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2094.

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According to the application of the computer network multimedia is more and more widely, and is also correspondingly strong shock by the network media and informatization for network communication and other requirements, progress and development of society constantly threatened by the impact of network multimedia communication. The paper theory-based with network multimedia communication to analysis the characteristics and performance requirements of network multimedia communication, combining to relevant material of the data flow, research and analyze the data flow of the mining technology, to carries on system analysis of the data flow, construct system model based on the network multimedia communication data stream, in order to better guide the dynamic analysis to network multimedia communication data flow, to master the multimedia communication data fusion and decision, so as to understand the users needs to network multimedia communication, to better guide the sustainable development of the network multimedia communication.

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18

Lin, Lin. "Multimedia data mining and retrieval for multimedia databases using associations and correlations." ACM SIGMultimedia Records 3, no. 1 (2011): 21–22. http://dx.doi.org/10.1145/2069196.2069200.

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19

R, Ankita. "Knowledge Discovery using Various Multimedia Data Mining Technique." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 3 (2015): 1138–41. http://dx.doi.org/10.17762/ijritcc2321-8169.150354.

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D, SARAVANAN. "LITERATURE SURVEY ON MULTIMEDIA DATA RETRIEVAL TECHNIQUES USING DATA MINING." i-manager’s Journal on Software Engineering 10, no. 3 (2016): 27. http://dx.doi.org/10.26634/jse.10.3.4902.

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21

Lu, Yu, and Wang Lizhi. "Construction of Multimedia Assisted Legal Classroom Teaching Model Based on Data Mining Algorithm." Scientific Programming 2021 (December 24, 2021): 1–11. http://dx.doi.org/10.1155/2021/9948800.

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In order to quickly and accurately retrieve a required part from massive multimedia educational resources and improve the utilization of educational resources, a multimedia assisted legal classroom teaching model based on data mining algorithm is designed. Firstly, the attributes of multimedia assisted legal classroom teaching resources are judged, and the numerical resources are standardized and discretized. Then, the B+ tree is used to establish the model’s indexes and index library, and the corresponding retrieval algorithm is designed to complete the resource search, establish the data distribution structure model of the multimedia assisted legal classroom teaching system, mine the data, reconstruct the phase space of the fused data information flow, extract the high-order moment features of the specific data in the multimedia assisted legal classroom teaching system in the reconstructed high-dimensional phase space, and realize the accurate mining of the feature data. The experimental results show that the teaching effect of the designed model has more advantages and can promote the improvement of students’ performance.

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Zhao, Hongxin. "Construction of Multimedia-Assisted English Teaching Mode in Big Data Network Environment." Wireless Communications and Mobile Computing 2021 (September 14, 2021): 1–10. http://dx.doi.org/10.1155/2021/1609187.

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Oral English teaching is the weakest link in multimedia English teaching at this stage. English teachers are constantly exploring effective approaches to improve oral English Teaching in their own educational practice. The big data multimedia English teaching mode conforms to embark on the historical stage. Firstly, this paper constructs the big data architecture of English teaching model mining and divides the construction of the teaching model into three parts: data mining, teaching model evaluation, and improvement optimization. Data mining uses the advanced DBN network to send data into the DBN-DELM network, which significantly improves the accuracy of the multimedia assisted English construction model. The simulation results show that teaching mode construction based on big data can effectively improve students’ interest in English learning; attitude; and oral English level including pronunciation, pronunciation and intonation, dialogue and communication, and oral expression and improve students’ group cooperation and communication ability, autonomous learning ability, evaluation consciousness, and ability.

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23

Wang, Dianhui, Yong-Soo Kim, Seok Cheon Park, Chul Soo Lee, and Yoon Kyung Han. "Learning Based Neural Similarity Metrics for Multimedia Data Mining." Soft Computing 11, no. 4 (2006): 335–40. http://dx.doi.org/10.1007/s00500-006-0086-2.

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Bhatt, Chidansh Amitkumar, and Mohan S. Kankanhalli. "Multimedia data mining: state of the art and challenges." Multimedia Tools and Applications 51, no. 1 (2010): 35–76. http://dx.doi.org/10.1007/s11042-010-0645-5.

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Qi, Shanshan. "Book Review “Multimedia data mining and analytics: disruptive innovation”." Information Technology & Tourism 16, no. 3 (2015): 321–22. http://dx.doi.org/10.1007/s40558-015-0043-y.

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Lv, Zhihan, Chen Zhong, and Dingde Jiang. "Guest Editorial: Smart Transportation Based on Multimedia Data Mining." Multimedia Tools and Applications 75, no. 24 (2016): 17443–48. http://dx.doi.org/10.1007/s11042-016-3915-z.

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Jeejo Vetharaj, J., S. Selvanayaki, and M. B.Suseela. "Classification and Privacy Preserving Search of Multimedia Data." International Journal of Engineering & Technology 7, no. 3.34 (2018): 259. http://dx.doi.org/10.14419/ijet.v7i3.34.18980.

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Classification, which is commonly used task in data mining applications separates the data present in the database based on some category. For years and years, considering the rise of several privacy issues, solutions in the form of theoretical and practical have been proposed for the classification problem under various security models. However, for the late Notoriety about cloud computing, clients presently have the chance on outsource their data, clinched alongside encrypted form, and also those information mining assignments of the cloud.. The data on the cloud which is in encrypted form, therefore existing privacy preserving classification techniques are not applicable. In this paper, we focus on finding solution for the classification problem over the encrypted data .Users can store their data with encryption by the use of ordered relational data. So, the data is obtained correctly without decrypting.

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Lin, Lin, Mei-Ling Shyu, and Shu-Ching Chen. "Rule-Based Semantic Concept Classification from Large-Scale Video Collections." International Journal of Multimedia Data Engineering and Management 4, no. 1 (2013): 46–67. http://dx.doi.org/10.4018/jmdem.2013010103.

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The explosive growth and increasing complexity of the multimedia data have created a high demand of multimedia services and applications in various areas so that people can access and distribute the data easily. Unfortunately, traditional keyword-based information retrieval is no longer suitable. Instead, multimedia data mining and content-based multimedia information retrieval have become the key technologies in modern societies. Among many data mining techniques, association rule mining (ARM) is considered one of the most popular approaches to extract useful information from multimedia data in terms of relationships between variables. In this paper, a novel rule-based semantic concept classification framework using weighted association rule mining (WARM), capturing the significance degrees of the feature-value pairs to improve the applicability of ARM, is proposed to deal with major issues and challenges in large-scale video semantic concept classification. Unlike traditional ARM that the rules are generated by frequency count and the items existing in one rule are equally important, our proposed WARM algorithm utilizes multiple correspondence analysis (MCA) to explore the relationships among features and concepts and to signify different contributions of the features in rule generation. To the authors best knowledge, this is one of the first WARM-based classifiers in the field of multimedia concept retrieval. The experimental results on the benchmark TRECVID data demonstrate that the proposed framework is able to handle large-scale and imbalanced video data with promising classification and retrieval performance.

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Wang, Fang. "The Effect of Multimedia Teaching Model of Music Course in Colleges and Universities Based on Classroom Audio Data Mining Technology." Tobacco Regulatory Science 7, no. 5 (2021): 4520–31. http://dx.doi.org/10.18001/trs.7.5.2.18.

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Objectives: With the rapid development of information technology, multimedia teaching mode carries a large amount of audio-visual information, quickly occupies the music classroom in Colleges and universities, and becomes the mainstream teaching mode of music teaching in Colleges and universities. Methods: Based on this, this study uses classroom audio data mining technology to analyze the effect of multimedia teaching mode of music courses in Colleges and universities. The method of audio data mining is analyzed in college music multimedia classroom. The advanced embedded SOPC system is used to decode the MP3 audio files played in music courses by combining software and hardware. The performance of the multimedia teaching system in college music courses is optimized. Results: The hardware resources are made use of the flexibility of SOPC (System-on-a-Programmable-Chip) system. Reasonable allocation achieves the optimal design of teaching mode. Finally, the superiority of the algorithm is verified by testing. The test results show that the decoding speed and efficiency of audio files can be significantly improved by combining hardware and software. Conclusion: At the same time, the system has greater flexibility and expandable space, which can effectively promote the multimedia teaching effect of music courses in Colleges and universities. The research in this paper is helpful to the flexible transformation of multimedia teaching mode of music courses in Colleges and universities, and provides an important reference for the popularization of multimedia and the wide use of data mining technology in music courses in Colleges and universities.

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Yan, Yilin, and Mei-Ling Shyu. "Correlation-Assisted Imbalance Multimedia Concept Mining and Retrieval." International Journal of Semantic Computing 11, no. 02 (2017): 209–27. http://dx.doi.org/10.1142/s1793351x17400098.

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In the past decades, we have witnessed an explosion of multimedia data, especially with the development of social media websites and blooming popularity of smart devices. As a result, multimedia semantic concept mining and retrieval whose objective is to mine useful information from the large amount of multimedia data including texts, images, and videos has become more and more important. The huge amount of multimedia data and the semantic gap between low-level features and high-level semantic concepts have made it even more challenging. To address these challenges, the correlations among the classes can provide important context cues to help bridge the semantic gap. Meanwhile, many real-world datasets do not have uniform class distributions while the minority instances actually represent the concept of interests, like frauds in transactions, intrusions in network security, and unusual events in surveillance. Despite extensive research efforts, imbalanced concept retrieval remains one of the most challenging research problems in multimedia data mining. Different from existing frameworks regarding concept correlations among labels, this paper presents a novel concept correlation analysis model using the correlation between the retrieval scores and labels. Experimental results on the TRECVID benchmark datasets demonstrate that the proposed framework can enhance imbalanced concept mining and retrieval even with trivial scores from the minority class.

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Li, Guo, and Wei Liu. "Multimedia Data Processing Technology and Application Based on Deep Learning." Advances in Multimedia 2023 (April 7, 2023): 1–15. http://dx.doi.org/10.1155/2023/4184425.

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With deep learning being widely used in various research fields, it is introduced into the research and analysis of multimedia data processing technology and application. First, the flow of multimedia data processing, the development of multimedia data, and the realization of multimedia data processing technology are explained and analyzed. Then, the related network results of deep learning (convolution network structure and countermeasure neural network structure) are put forward, and the image comparison of the activation function and the loss function of deep learning is analyzed, which provides functional algorithm support for the experimental analysis of deep learning in multimedia data processing technology. Finally, through the analysis of experimental data, it is concluded that deep learning has stronger advantages in the application research of multimedia data processing technology compared with other learning methods. In the multimedia data processing, the multimedia data processing technology is obviously superior to the data mining technology and data compression technology. Finally, under the support of deep learning data, we conclude that multimedia data processing technology is widely used and quoted in various fields. Therefore, with the development of multimedia, the amount of multimedia data is increasing; so, we should vigorously develop multimedia data processing technology in an all-round way.

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Heng, Shao. "A New Intelligent Optimization Network Online Learning Behavior in Multimedia Big Data Environment." International Journal of Mobile Computing and Multimedia Communications 8, no. 3 (2017): 21–31. http://dx.doi.org/10.4018/ijmcmc.2017070102.

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In the era of multimedia big data, the online learning behavior of users becomes rich and colorful. This paper proposes a hybrid clonal selection differential evolution optimization algorithm based on clonal selection algorithm and differential evolution in multimedia big data environment. The proposed intelligent optimization algorithm treats each e-learning behavior of the user as an antibody, and gets the best results of multimedia big data mining by a number of iterative searches. Experimental results show the feasibility and effectiveness of the proposed intelligent optimization algorithm.

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Du, Yishan, and Tianzhong Zhao. "Network Teaching Technology Based on Big Data Mining and Information Fusion." Security and Communication Networks 2021 (February 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/6629563.

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With the continuous development of modern multimedia technology, the integration of computer technology into the teaching of various subjects has become a trend of the times. The application of computer media and network technology in mathematics teaching improves the integration of mathematics teaching and the integration of resources. A mathematics teaching network media fusion technology is proposed based on big data mining and information fusion, which combines the characteristics of multimedia and network technology in opening, creativity, subjectivity, and so on, and the database model of mathematics teaching is constructed. The multithread integrated scheduling method is used to design the mathematics teaching database model, the fuzzy control method is used to control the multimedia in mathematics teaching, and the big data association rule mining method is used to realize the information fusion of mathematics teaching resources. The optimization and integration of mathematics teaching resources and adaptive scheduling are realized under the technology of computer media and network, and the level of mathematics teaching is improved. The test results show that using this method to design the computer network media of mathematics teaching has a better ability of integrating and dispatching mathematics teaching resources, and the integration of mathematics teaching resources is stronger, which promotes the improvement of mathematics teaching level.

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B. T., Adetoba, Awodele O, and Kuyoro S.O. "A Multimedia Data Mining Framework for Monitoring E-Examination Environment." International journal of Multimedia & Its Applications 9, no. 3 (2017): 25–34. http://dx.doi.org/10.5121/ijma.2017.9303.

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Sun, Yingxin. "Multimedia Technology of Spatial Data Mining Based on Genetic Algorithm." Computational Intelligence and Neuroscience 2022 (May 21, 2022): 1–8. http://dx.doi.org/10.1155/2022/4835359.

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In order to make key decisions more conveniently according to the massive data information obtained, a spatial data mining technology based on a genetic algorithm is proposed, which is combined with the k-means algorithm. The immune principle and adaptive genetic algorithm are introduced to optimize the traditional genetic algorithm, and the K-means, GK, and IGK algorithms are compared and analyzed. The results show that, in two different datasets, the objective functions obtained by the K-means algorithm are 94.05822 and 4.10373 × 10 6 , respectively, while the objective functions obtained by the GK and IGK algorithms are 89.8619 and 3.9088 × 10 6 , respectively. The difference between the three algorithms can also be reflected in the data comparison of the number of iterations. The number of iterations required for k-means to reach the optimal solution is 8.21 and 8.4, respectively, which is the most among the three algorithms, while the number of iterations required for IGK to reach the optimal solution is 5.84 and 4.9, respectively, which is the least. Although the time required for K-means is short, by comparison, the IGK algorithm we use can get the optimal solution in relatively less time.

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Ravi, M., M. Ekambaram Naidu, and G. Narsimha. "An Optimized Soft Computing based Approach for Multimedia Data Mining." International Journal of Business Intelligence and Data Mining 1, no. 1 (2023): 1. http://dx.doi.org/10.1504/ijbidm.2023.10046450.

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37

Zhang, Zhongfei, Florent Masseglia, Ramesh JAIN, and Alberto Del Bimbo. "Editorial: Introduction to the Special Issue on Multimedia Data Mining." IEEE Transactions on Multimedia 10, no. 2 (2008): 165–66. http://dx.doi.org/10.1109/tmm.2007.915372.

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Bouali, Fatma, Latifur Khan, and Florent Masseglia. "The 6th international workshop on Multimedia Data Mining (MDM/KDD2005)." ACM SIGKDD Explorations Newsletter 7, no. 2 (2005): 148–50. http://dx.doi.org/10.1145/1117454.1117478.

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Khan, Latifur, and Valery A. Petrushin. "The 5th international workshop on multimedia data mining (MDM/KDD2004)." ACM SIGKDD Explorations Newsletter 6, no. 2 (2004): 144–46. http://dx.doi.org/10.1145/1046456.1046483.

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Petrushin, Valery A., Anne Kao, and Latifur Khan. "The 4th international workshop on multimedia data mining (MDM/KDD2003)." ACM SIGKDD Explorations Newsletter 6, no. 1 (2004): 106–8. http://dx.doi.org/10.1145/1007730.1007743.

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Sang, Jitao, Yue Gao, Bing-kun Bao, Cees Snoek, and Qionghai Dai. "Recent advances in social multimedia big data mining and applications." Multimedia Systems 22, no. 1 (2015): 1–3. http://dx.doi.org/10.1007/s00530-015-0482-5.

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Ravi, M., M. Ekambaram Naidu, and G. Narsimha. "An optimised soft computing-based approach for multimedia data mining." International Journal of Business Intelligence and Data Mining 22, no. 4 (2023): 410. http://dx.doi.org/10.1504/ijbidm.2023.130599.

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Zhu, Dingju. "Big data-based multimedia transcoding method and its application in multimedia data mining-based smart transportation and telemedicine." Multimedia Tools and Applications 75, no. 24 (2016): 17647–68. http://dx.doi.org/10.1007/s11042-016-3466-3.

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Naaman, Mor. "Social multimedia: highlighting opportunities for search and mining of multimedia data in social media applications." Multimedia Tools and Applications 56, no. 1 (2010): 9–34. http://dx.doi.org/10.1007/s11042-010-0538-7.

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He, Han, Yuanyuan Hong, Weiwei Liu, and Sung-A. Kim. "Data mining model for multimedia financial time series using information entropy." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 5339–45. http://dx.doi.org/10.3233/jifs-189019.

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At present, KDD research covers many aspects, and has achieved good results in the discovery of time series rules, association rules, classification rules and clustering rules. KDD has also been widely used in practical work such as OLAP and DW. Also, with the rapid development of network technology, KDD research based on WEB has been paid more and more attention. The main research content of this paper is to analyze and mine the time series data, obtain the inherent regularity, and use it in the application of financial time series transactions. In the financial field, there is a lot of data. Because of the huge amount of data, it is difficult for traditional processing methods to find the knowledge contained in it. New knowledge and new technology are urgently needed to solve this problem. The application of KDD technology in the financial field mainly focuses on customer relationship analysis and management, and the mining of transaction data is rare. The actual work requires a tool to analyze the transaction data and find its inherent regularity, to judge the nature and development trend of the transaction. Therefore, this paper studies the application of KDD in financial time series data mining, explores an appropriate pattern mining method, and designs an experimental system which includes mining trading patterns, analyzing the nature of transactions and predicting the development trend of transactions, to promote the application of KDD in the financial field.

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Li, Juan, and Geng Sun. "A rational resource allocation method for multimedia network teaching reform based on Bayesian partition data mining." Electronic Research Archive 31, no. 10 (2023): 5959–75. http://dx.doi.org/10.3934/era.2023303.

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<abstract> <p>In order to improve the application of teaching resources and reduce delays in the integration process of multimedia network, a rational resource allocation method for multimedia network teaching reform based on Bayesian partition data mining is proposed. Bayesian partition is used to preprocess the multimedia network teaching resources (MNTR), adjusting the recognition probability of MNTR in each partition based on its attributes. By performing Bayesian quantitative classification using samples of MNTR, the prior probability is adjusted through maximization analysis. The partitioned resources undergo sample data mining to obtain the data category collection of all MNTR. A prediction model is then built to forecast the demand for teaching resources at specific times in the future. MNTR can be rationally allocated based on the prediction results. Experimental results demonstrate that this method reduces delays in MNTR application and improves the accuracy and utilization of teaching resources.</p> </abstract>

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Chen, Yu-Na, and Xuesen Zhang. "Evaluation of Multimedia Courseware-Assisted Teaching Effect of Medical Images Based on the Deep Learning Algorithm." Journal of Environmental and Public Health 2022 (September 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/5991087.

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In order to improve the dynamic evaluation ability of medical image multimedia courseware-assisted teaching effect, the evaluation of medical image multimedia courseware-assisted teaching effect based on a deep learning algorithm is proposed. The statistical data analysis model of medical image multimedia courseware-assisted teaching effect is established to estimate its utilization rate and scale parameters. Based on the prediction of spatial attribute parameters, the classification big data mining model of medical image multimedia courseware-assisted teaching is constructed by using the deep learning algorithm, mining association rules and frequent item sets that can dynamically reflect the quality of medical image multimedia courseware-assisted teaching, and extracting the statistical feature of the dataset of constraint indicators of medical image multimedia courseware-assisted teaching effect to improve the teaching quality of medical imaging course. The simulation results show that this method has a better precision delivery effect, higher dynamic matching degree of teaching evaluation parameters, more than 90% reliability, and better clustering of statistical eigenvalues.

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Duran, Zekeriya, İsmail Akargöl, and Tuğba Doğan. "Data Mining, Weka Decision Trees." Orclever Proceedings of Research and Development 3, no. 1 (2023): 401–16. http://dx.doi.org/10.56038/oprd.v3i1.376.

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Nowadays, computer technologies are increasing rapidly. Thanks to the development of computer technologies, large and complex raw data sets can be transformed into useful information with different analysis techniques. Different algorithms developed thanks to computer technologies can offer different solutions to scientists and users working in different branches of science, especially engineering sciences, mathematics, medicine, industry, financial/economic fields, marketing, education, multimedia and statistics. Thanks to these solutions, it is possible to easily achieve the desired goals and objectives. Thus, by correctly managing and analyzing existing data in large and complex raw data datasets, accurate predictions can be made to be used in similar problems in the future. Data sets are analyzed and evaluated using different methods. It is also possible that the classification of data during the analysis and evaluation stages of data sets significantly affects the decision-making process regarding the work to be done. Classification of data can be done by statistical method or data mining method. Decision trees, which can be used to classify numerical and alphanumeric data, generally provide a great advantage for decision makers in terms of easy interpretation and understandability compared to other classification techniques. For these reasons, in this study, decision trees, one of the most used classification techniques in data mining, are mentioned.

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Kulvinder Singh, Et al. "Enhancing Multimodal Information Retrieval Through Integrating Data Mining and Deep Learning Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 560–69. http://dx.doi.org/10.17762/ijritcc.v11i9.8844.

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Multimodal information retrieval, the task of re trieving relevant information from heterogeneous data sources such as text, images, and videos, has gained significant attention in recent years due to the proliferation of multimedia content on the internet. This paper proposes an approach to enhance multimodal information retrieval by integrating data mining and deep learning techniques. Traditional information retrieval systems often struggle to effectively handle multimodal data due to the inherent complexity and diversity of such data sources. In this study, we leverage data mining techniques to preprocess and structure multimodal data efficiently. Data mining methods enable us to extract valuable patterns, relationships, and features from different modalities, providing a solid foundation for sub- sequent retrieval tasks. To further enhance the performance of multimodal information retrieval, deep learning techniques are employed. Deep neural networks have demonstrated their effectiveness in various multimedia tasks, including image recognition, natural language processing, and video analysis. By integrating deep learning models into our retrieval framework, we aim to capture complex intermodal dependencies and semantically rich representations, enabling more accurate and context-aware retrieval.

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Ushaa, Eswaran. "Optimizing database efficiency: Empowering systems with data mining." i-manager's Journal on Information Technology 12, no. 3 (2023): 32. http://dx.doi.org/10.26634/jit.12.3.20050.

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Databases are critical for storing structured data, but deriving insights remains challenging. This paper investigates integrating classification, clustering, association rules, and anomaly detection within database architectures to enable intelligent analytics. A unified architecture is proposed along with an asynchronous incremental learning technique to efficiently handle dynamic data. Comprehensive experiments on diverse real-world datasets demonstrate 10–25% improvements in metrics like query latency, accuracy, and costs compared to conventional integration approaches. Emerging applications in multimedia, spatiotemporal, and IoT mining are discussed. The holistic convergence of multiple techniques is highlighted as the key innovation in progressing towards next-generation intelligent databases powered by analytics.

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