Bibliografías temáticas / Hidden Data Mining

Literatura académica sobre el tema "Hidden Data Mining"

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Publicado: 27 de enero de 2024

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Artículos de revistas sobre el tema "Hidden Data Mining"

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Wang, Lidong y Guanghui Wang. "Data Mining Applications in Big Data". Computer Engineering and Applications Journal 4, n.º 3 (20 de septiembre de 2015): 143–52. http://dx.doi.org/10.18495/comengapp.v4i3.155.

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Data mining is a process of extracting hidden, unknown, but potentially useful information from massive data. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in data mining and technologies in Big Data. Challenges of data mining and data mining with big data are discussed. Some technology progress of data mining and data mining with big data are also presented.
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Maryoosh, Amal Abdulbaqi y Enas Mohammed Hussein. "A Review: Data Mining Techniques and Its Applications". International Journal of Computer Science and Mobile Applications 10, n.º 3 (30 de marzo de 2022): 1–14. http://dx.doi.org/10.47760/ijcsma.2022.v10i03.001.

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Data mining is a set of processes by which knowledge is extracted from huge amounts of data. Data mining is used to extract useful patterns and hidden information from this data. Machine learning techniques help in the comprehension of the hidden knowledge in the data. Data mining is considered an important field of research and is used in many different fields such as fraud detection, financial banking, education, healthcare, agriculture, industry, etc. In this paper, we will highlight some fundamentals of data mining and its applications. Also, we will conduct a comparative study among different reviews, combining literary studies that employed data mining techniques in various fields and reviewing the latest developments in this field.
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Sharma, Pragati y Dr Sanjiv Sharma. "DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW". International Journal of Engineering Technologies and Management Research 5, n.º 2 (1 de mayo de 2020): 166–77. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.641.

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Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining can be used for decision making in higher educational institutions. This paper is based on literature review of different data mining techniques along with certain algorithms like classification, clustering etc. This paper represents the effectiveness of mining techniques with educational data set for higher education institutions.
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Carpenter, Chris. "Data Mining of Hidden Danger in Operational Production". Journal of Petroleum Technology 71, n.º 08 (1 de agosto de 2019): 71–78. http://dx.doi.org/10.2118/0819-0071-jpt.

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Wang, Zhi Yan, Bei Zhan Wang y Yi Dong Wang. "Data Mining Technology Applied in Network Security". Advanced Materials Research 989-994 (julio de 2014): 4974–79. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4974.

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As the Internet is developing fast, network security issues are rising. Data mining technology is applied to the analysis and understanding data, revealing hidden data secret inside knowledge. Especially high dimensional data mining can be used in information security data analyze, making the study of high-dimensional data mining very important. In this paper, traditional data mining is introduced; the concept and core ideas of high dimensional data mining are described, as well as its applications in network security.
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Liu, Jing, Qing Xiang Zhu, Xin Yu, Jing Xin Wang y Yi Ge Huang. "The Research of Warning Model of Hidden Failure Based on Data Mining". Key Engineering Materials 693 (mayo de 2016): 1844–48. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1844.

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Complex equipment is mainly used in important areas of national defense, health care, banking, etc. Consequences of failure are relatively severe, while the hidden failures are contained in the most complex devices as the process is running. Hidden failures in the normal operation of the device is difficult to find, and only under certain conditions will be triggered, while other faults may be led. The stability of the running system will be undermined. In order to monitor the occurrence and development of hidden failure of complex equipment, a hidden failure warning model based on data mining has been put forward, and the theory of the model has been analyzed, the selection gist of the model parameters has been given. The result shows that the accuracy of hidden failure impact value forecast by the model is 93.33%, the impact degree of the hidden failure effect on the dominant failure can be effectively monitored, and the model makes a good preventative effect against the sudden failure caused by the hidden failure.
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Bathla, Gourav, Himanshu Aggarwal y Rinkle Rani. "Migrating From Data Mining to Big Data Mining". International Journal of Engineering & Technology 7, n.º 3.4 (25 de junio de 2018): 13. http://dx.doi.org/10.14419/ijet.v7i3.4.14667.

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Data mining is one of the most researched fields in computer science. Several researches have been carried out to extract and analyse important information from raw data. Traditional data mining algorithms like classification, clustering and statistical analysis can process small scale of data with great efficiency and accuracy. Social networking interactions, business transactions and other communications result in Big data. It is large scale of data which is not in competency for traditional data mining techniques. It is observed that traditional data mining algorithms are not capable for storage and processing of large scale of data. If some algorithms are capable, then response time is very high. Big data have hidden information, if that is analysed in intelligent manner can be highly beneficial for business organizations. In this paper, we have analysed the advancement from traditional data mining algorithms to Big data mining algorithms. Applications of traditional data mining algorithms can be straight forward incorporated in Big data mining algorithm. Several studies have analysed traditional data mining with Big data mining, but very few have analysed most important algortihsm within one research work, which is the core motive of our paper. Readers can easily observe the difference between these algorthithms with pros and cons. Mathemtics concepts are applied in data mining algorithms. Means and Euclidean distance calculation in Kmeans, Vectors application and margin in SVM and Bayes therorem, conditional probability in Naïve Bayes algorithm are real examples. Classification and clustering are the most important applications of data mining. In this paper, Kmeans, SVM and Naïve Bayes algorithms are analysed in detail to observe the accuracy and response time both on concept and empirical perspective. Hadoop, Mapreduce etc. Big data technologies are used for implementing Big data mining algorithms. Performace evaluation metrics like speedup, scaleup and response time are used to compare traditional mining with Big data mining.
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Tang, Yu y Guo Hui Li. "Data Mining and Visualization System Design and Development". Advanced Materials Research 971-973 (junio de 2014): 1444–48. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1444.

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With the rise of the network,everyday the video websites update plenty of video datas.Faced with a lot of video datas,if you only rely on the human to analyze the video datas in order to dig out the information hidden in the video room,it will take a lot of time and is difficult to achieve the desired result. This paper develops a data mining and visualization system,which visualized shows the relationship between the video datas through a network graph of nodes.Based on visualized showing the relationship between the video datas,the system provides the tool to analyze the video datas and dig out the information hidden in the video room.
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Chunfeng Liu, Shanshan Kong, Li Feng y Yuqian Kang. "Outer P-sets and F- mining of Hidden Data". International Journal of Advancements in Computing Technology 4, n.º 17 (30 de septiembre de 2012): 180–87. http://dx.doi.org/10.4156/ijact.vol4.issue17.21.

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Gozali, Elahe, Bahlol Rahimi, Malihe Sadeghi y Reza Safdari. "Diagnosis of diseases using data mining". Medical Technologies Journal 1, n.º 4 (29 de noviembre de 2017): 120–21. http://dx.doi.org/10.26415/2572-004x-vol1iss4p120-121.

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Introduction: In the information age, data are the most important asset for health organizations. In the case of using data in useful and optimal manner, they can become financial resources for organization. Data mining is an appropriate method to transform this potential value into strategic information. Data mining means extraction of hidden information, recognition of hidden relationships and patterns, and in general, discovery of useful knowledge at high volume. The objective of this review paper was to evaluate using data mining in diagnoses of diseases. Methods: This research is a review paper conducted based on a structured review of the papers published in Science Direct, Pubmed, Google Scholar, SID, Magiran (between years 2005 and 2015) and books related to using data mining in medical science and using it in diagnose of diseases with related keywords. Results: Nowadays, data mining is used in many medical science studies, including diagnosis of diseases, discovering the hidden patterns in data, and so on. New ideas such as discovery of Knowledge from Discovery and Data Mining Database, which includes data mining techniques, have found more popularity and they has becomedesired research tool for researchers. Researchers can use them to identify patterns and relationshipsamong great number of variables. Using them, researchers have been able to predict theresults obtained from one disease by using information stores available in databases. Several studies have indicated that data mining is used widely in diagnosis of diseases based on types of information (medical images, characteristics of patients, and so on), such as tuberculosis, types of cancers, infectious diseases, and diagnosis of anomalies rarely diagnosed by human (spots and particular points within aye, which is the symptom of onset of blindness resulting from diabetes), determining type of behavior with patients, and predicting the success rate of surgical surgeries, determining the success rate of therapeutic methods in coping with incurable diseases, and so on. Conclusion: One of the most important challenging topics in healthcare is transformation of raw clinical data into meaningful information following continuous generation of great number of data. In current competitive environment, health organizations using technologies such as data mining to improve healthcare quality will achieve success faster. Many of research centers in Iran are faced with large volume of information, which is not analyzed at all or will be time-consuming due to using traditional methods, even in the case of using analysis and converting them to knowledge. In light of using data mining and its implementation, health organizations can transform the data into a powerful and competitive tool and take new steps in preventing, diagnosing, treating, and providing high-quality services for clients.
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Tesis sobre el tema "Hidden Data Mining"

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Liu, Tantan. "Data Mining over Hidden Data Sources". The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343313341.

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Dharmavaram, Sirisha. "Mining Biomedical Data for Hidden Relationship Discovery". Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1538709/.

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With an ever-growing number of publications in the biomedical domain, it becomes likely that important implicit connections between individual concepts of biomedical knowledge are overlooked. Literature based discovery (LBD) is in practice for many years to identify plausible associations between previously unrelated concepts. In this paper, we present a new, completely automatic and interactive system that creates a graph-based knowledge base to capture multifaceted complex associations among biomedical concepts. For a given pair of input concepts, our system auto-generates a list of ranked subgraphs uncovering possible previously unnoticed associations based on context information. To rank these subgraphs, we implement a novel ranking method using the context information obtained by performing random walks on the graph. In addition, we enhance the system by training a Neural Network Classifier to output the likelihood of the two concepts being likely related, which provides better insights to the end user.
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Liu, Zhenjiao. "Incomplete multi-view data clustering with hidden data mining and fusion techniques". Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS011.

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Le regroupement de données multivues incomplètes est un axe de recherche majeur dans le domaines de l'exploration de données et de l'apprentissage automatique. Dans les applications pratiques, nous sommes souvent confrontés à des situations où seule une partie des données modales peut être obtenue ou lorsqu'il y a des valeurs manquantes. La fusion de données est une méthode clef pour l'exploration d'informations multivues incomplètes. Résoudre le problème de l'extraction d'informations multivues incomplètes de manière ciblée, parvenir à une collaboration flexible entre les vues visibles et les vues cachées partagées, et améliorer la robustesse sont des défis. Cette thèse se concentre sur trois aspects : l'exploration de données cachées, la fusion collaborative et l'amélioration de la robustesse du regroupement. Les principales contributions sont les suivantes:1) Exploration de données cachées pour les données multi-vues incomplètes : les algorithmes existants ne peuvent pas utiliser pleinement l'observation des informations dans et entre les vues, ce qui entraîne la perte d'une grande quantité d'informations. Nous proposons donc un nouveau modèle de regroupement multi-vues incomplet IMC-NLT (Incomplete Multi-view Clustering Based on NMF and Low-Rank Tensor Fusion) basé sur la factorisation de matrices non négatives et la fusion de tenseurs de faible rang. IMC-NLT utilise d'abord un tenseur de faible rang pour conserver les caractéristiques des vues avec une dimension unifiée. En utilisant une mesure de cohérence, IMC-NLT capture une représentation cohérente à travers plusieurs vues. Enfin, IMC-NLT intègre plusieurs apprentissages dans un modèle unifié afin que les informations cachées puissent être extraites efficacement à partir de vues incomplètes. Des expériences sur cinq jeux de données ont validé les performances d'IMC-NLT.2) Fusion collaborative pour les données multivues incomplètes : notre approche pour résoudre ce problème est le regroupement multivues incomplet par représentation à faible rang. L'algorithme est basé sur une représentation éparse de faible rang et une représentation de sous-espace, dans laquelle les données manquantes sont complétées en utilisant les données d'une modalité et les données connexes d'autres modalités. Pour améliorer la stabilité des résultats de clustering pour des données multi-vues avec différents degrés de manquants, CCIM-SLR utilise le modèle Γ-norm, qui est une méthode de représentation à faible rang ajustable. CCIM-SLR peut alterner entre l'apprentissage de la vue cachée partagée, la vue visible et les partitions de clusters au sein d'un cadre d'apprentissage collaboratif. Un algorithme itératif avec convergence garantie est utilisé pour optimiser la fonction objective proposée.3) Amélioration de la robustesse du regroupement pour les données multivues incomplètes : nous proposons une fusion de la convolution graphique et des goulots d'étranglement de l'information (apprentissage de la représentation multivues incomplète via le goulot d'étranglement de l'information). Nous introduisons la théorie du goulot d'étranglement de l'information afin de filtrer les données parasites contenant des détails non pertinents et de ne conserver que les éléments les plus pertinents. Nous intégrons les informations sur la structure du graphe basées sur les points d'ancrage dans les informations sur le graphe local. Le modèle intègre des représentations multiples à l'aide de goulets d'étranglement de l'information, réduisant ainsi l'impact des informations redondantes dans les données. Des expériences approfondies sont menées sur plusieurs ensembles de données du monde réel, et les résultats démontrent la supériorité de IMRL-AGI. Plus précisément, IMRL-AGI montre des améliorations significatives dans la précision du clustering et de la classification, même en présence de taux élevés de données manquantes par vue (par exemple, 10,23 % et 24,1% respectivement sur l'ensemble de données ORL)
Incomplete multi-view data clustering is a research direction that attracts attention in the fields of data mining and machine learning. In practical applications, we often face situations where only part of the modal data can be obtained or there are missing values. Data fusion is an important method for incomplete multi-view information mining. Solving incomplete multi-view information mining in a targeted manner, achieving flexible collaboration between visible views and shared hidden views, and improving the robustness have become quite challenging. This thesis focuses on three aspects: hidden data mining, collaborative fusion, and enhancing the robustness of clustering. The main contributions are as follows:1. Hidden data mining for incomplete multi-view data: existing algorithms cannot make full use of the observation of information within and between views, resulting in the loss of a large amount of valuable information, and so we propose a new incomplete multi-view clustering model IMC-NLT (Incomplete Multi-view Clustering Based on NMF and Low-Rank Tensor Fusion) based on non-negative matrix factorization and low-rank tensor fusion. IMC-NLT first uses a low-rank tensor to retain view features with a unified dimension. Using a consistency measure, IMC-NLT captures a consistent representation across multiple views. Finally, IMC-NLT incorporates multiple learning into a unified model such that hidden information can be extracted effectively from incomplete views. We conducted comprehensive experiments on five real-world datasets to validate the performance of IMC-NLT. The overall experimental results demonstrate that the proposed IMC-NLT performs better than several baseline methods, yielding stable and promising results.2. Collaborative fusion for incomplete multi-view data: our approach to address this issue is Incomplete Multi-view Co-Clustering by Sparse Low-Rank Representation (CCIM-SLR). The algorithm is based on sparse low-rank representation and subspace representation, in which jointly missing data is filled using data within a modality and related data from other modalities. To improve the stability of clustering results for multi-view data with different missing degrees, CCIM-SLR uses the Γ-norm model, which is an adjustable low-rank representation method. CCIM-SLR can alternate between learning the shared hidden view, visible view, and cluster partitions within a co-learning framework. An iterative algorithm with guaranteed convergence is used to optimize the proposed objective function. Compared with other baseline models, CCIM-SLR achieved the best performance in the comprehensive experiments on the five benchmark datasets, particularly on those with varying degrees of incompleteness.3. Enhancing the clustering robustness for incomplete multi-view data: we offer a fusion of graph convolution and information bottlenecks (Incomplete Multi-view Representation Learning Through Anchor Graph-based GCN and Information Bottleneck - IMRL-AGI). First, we introduce the information bottleneck theory to filter out the noise data with irrelevant details and retain only the most relevant feature items. Next, we integrate the graph structure information based on anchor points into the local graph information of the state fused into the shared information representation and the information representation learning process of the local specific view, a process that can balance the robustness of the learned features and improve the robustness. Finally, the model integrates multiple representations with the help of information bottlenecks, reducing the impact of redundant information in the data. Extensive experiments are conducted on several real-world datasets, and the results demonstrate the superiority of IMRL-AGI. Specifically, IMRL-AGI shows significant improvements in clustering and classification accuracy, even in the presence of high view missing rates (e.g. 10.23% and 24.1% respectively on the ORL dataset)
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Peng, Yingli. "Improvement of Data Mining Methods on Falling Detection and Daily Activities Recognition". Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-25521.

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With the growing phenomenon of an aging population, an increasing numberof older people are living alone for domestic and social reasons. Based on thisfact, falling accidents become one of the most important factors in threateningthe lives of the elderly. Therefore, it is necessary to set up an application to de-tect the daily activities of the elderly. However, falling detection is difficult to recognize because the "falling" motion is an instantaneous motion and easy to confuse with others.In this thesis, three data mining methods were employed on wearable sensors' value; first which contains the continuous data set concerning eleven activities of daily living, and then an analysis of the different results was performed. Not only could the fall be detected, but other activities could also be classified. In detail, three methods including Back Propagation Neural Network, Support Vector Machine and Hidden Markov Model are applied separately to train the data set.What highlights the project is that a new  idea is put forward, the aim of which is to design a methodology of accurate classification in the time-series data set. The proposed approach, which includes obtaining of classifier parts and the application parts allows the generalization of classification. The preliminary results indicate that the new method achieves the high accuracy of classification,and significantly performs better than other data mining methods in this experiment.
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Yang, Yimin. "Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis". FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2254.

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Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.
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Sajeva, Lisa. "Predizione del tempo rimanente di vita di un impianto mediante Hidden Markow Model". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13846/.

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In this thesis we investigate the main methods used in the literature for the automation of conditio-base maintenance and then see a pratical application concerning bearing system. In the specifics we first analyze the row signal of vibration decomposing whit a wavelet packet transform then, we select the best level and index in term of characteristics. For create a model of failure we use the method of Hidden Markov Model. At least we compare the model generated with other level and index of decomposition to demonstrate that our choice was the best.
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Vitali, Federico. "Map-Matching su Piattaforma BigData". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18089/.

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Nell'ambito dell'analisi dei dati di movimento atto all'estrazione di informazioni utili, il map matching ha l'obiettivo di proiettare i punti GPS generati dagli oggetti in movimento sopra i segmenti stradali in modo da rappresentare l'attuale posizione degli oggetti. Fino ad ora, il map matching è stato sfruttato in ambiti come l'analisi del traffico, l'estrazione dei percorsi frequenti e la predizione della posizione degli oggetti, oltre a rappresentare un'importante fase di pre-processing nell'intero procedimento di trajectory mining. Sfortunatamente, le implementazioni allo stato dell'arte degli algoritmi di map matching sono tutte sequenziali o inefficienti. In questa tesi viene quindi proposto un algoritmo il quale si basa su di un algoritmo sequenziale conosciuto per la sua accuratezza ed efficienza il quale viene completamente riformulato in maniera distribuita in modo tale da raggiungere anche un elevata scalabilità nel caso di utilizzo con i big data. Inoltre, viene migliorata la robustezza dell'algoritmo, il quale è basato sull'Hidden Markov Model di primo ordine, introducendo una strategia per gestire i possibili buchi di informazione che si possono venire a creare tra i segmenti stradali assegnati. Infatti, il problema può accadere in caso di campionamento variabile dei punti GPS in aree urbane con un elevata frammentazione dei segmenti stradali. L'implementazione è basata su Apache Spark e testata su un dataset di oltre 7.8 milioni di punti GPS nella città di Milano.
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Eng, Catherine. "Développement de méthodes de fouille de données basées sur les modèles de Markov cachés du second ordre pour l'identification d'hétérogénéités dans les génomes bactériens". Thesis, Nancy 1, 2010. http://www.theses.fr/2010NAN10041/document.

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Les modèles de Markov d’ordre 2 (HMM2) sont des modèles stochastiques qui ont démontré leur efficacité dans l’exploration de séquences génomiques. Cette thèse explore l’intérêt de modèles de différents types (M1M2, M2M2, M2M0) ainsi que leur couplage à des méthodes combinatoires pour segmenter les génomes bactériens sans connaissances a priori du contenu génétique. Ces approches ont été appliquées à deux modèles bactériens afin d’en valider la robustesse : Streptomyces coelicolor et Streptococcus thermophilus. Ces espèces bactériennes présentent des caractéristiques génomiques très distinctes (composition, taille du génome) en lien avec leur écosystème spécifique : le sol pour les S. coelicolor et le milieu lait pour S. thermophilus
Second-order Hidden Markov Models (HMM2) are stochastic processes with a high efficiency in exploring bacterial genome sequences. Different types of HMM2 (M1M2, M2M2, M2M0) combined to combinatorial methods were developed in a new approach to discriminate genomic regions without a priori knowledge on their genetic content. This approach was applied on two bacterial models in order to validate its achievements: Streptomyces coelicolor and Streptococcus thermophilus. These bacterial species exhibit distinct genomic traits (base composition, global genome size) in relation with their ecological niche: soil for S. coelicolor and dairy products for S. thermophilus. In S. coelicolor, a first HMM2 architecture allowed the detection of short discrete DNA heterogeneities (5-16 nucleotides in size), mostly localized in intergenic regions. The application of the method on a biologically known gene set, the SigR regulon (involved in oxidative stress response), proved the efficiency in identifying bacterial promoters. S. coelicolor shows a complex regulatory network (up to 12% of the genes may be involved in gene regulation) with more than 60 sigma factors, involved in initiation of transcription. A classification method coupled to a searching algorithm (i.e. R’MES) was developed to automatically extract the box1-spacer-box2 composite DNA motifs, structure corresponding to the typical bacterial promoter -35/-10 boxes. Among the 814 DNA motifs described for the whole S. coelicolor genome, those of sigma factors (B, WhiG) could be retrieved from the crude data. We could show that this method could be generalized by applying it successfully in a preliminary attempt to the genome of Bacillus subtilis
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陳迪祥. "A Data Mining Approach to Eliciting Hidden Relationships from Disease Data". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/33856707588342488454.

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碩士
國立暨南國際大學
資訊管理學系
91
Data mining is able to find some unobvious or hidden information from data and it is what the managers of hospitals need for their rich data. There are many kinds of data in those hospitals’ database, such as records of emergency treatment, records of outpatient services, records of examining patients, and records of taking medicines. The data is helpful for exploring medical knowledge by data mining technology. This paper describes a data mining system which processing the standard health insurance files defined by Bureau of National Health Insurance. The system uses FP-Tree for good performance of mining. A distributed and caching architecture has been implemented in the system to balance the loading of mining. Users can acquire mining results from the system quickly. The system will elicit hidden relationships within diseases from those health insurance files. Our frequent patterns also include conditional probabilities that certain diseases may happen if the patient has some disease. Doctors and researchers operate the system by a browser. The mining results discovered by the system will help doctors and researchers with medical researches. Keywords: Data mining, Health Insurance, Medicine, Distributed Architecture
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Yu, Zhun. "Mining Hidden Knowledge from Measured Data for Improving Building Energy Performance". Thesis, 2012. http://spectrum.library.concordia.ca/973713/1/Yu_PhD_S2012.pdf.

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Nowadays, building automation and energy management systems provide an opportunity to collect vast amounts of building-related data (e.g., climatic data, building operational data, etc.). The data can provide abundant useful knowledge about the interactions between building energy consumption and its influencing factors. Such interactions play a crucial role in developing and implementing control strategies to improve building energy performance. However, the data is rarely analyzed and this useful knowledge is seldom extracted due to a lack of effective data analysis techniques. In this research, data mining (classification analysis, cluster analysis, and association rule mining) is proposed to extract hidden useful knowledge from building-related data. Moreover, a data analysis process and a data mining framework are proposed, enabling building-related data to be analyzed more efficiently. The applications of the process and framework to two sets of collected data demonstrate their applicability. Based on the process and framework, four data analysis methodologies were developed and applied to the collected data. Classification analysis was applied to develop a methodology for establishing building energy demand predictive models. To demonstrate its applicability, the methodology was applied to estimate residential building energy performance indexes by modeling building energy use intensity (EUI) levels (either high or low). The results demonstrate that the methodology can classify and predict the building energy demand levels with an accuracy of 93% for training data and 92% for test data, and identify and rank significant factors of building EUI automatically. Cluster analysis was used to develop a methodology for examining the influences of occupant behavior on building energy consumption. The results show that the methodology facilitates the evaluation of building energy-saving potential by improving the behavior of building occupants, and provides multifaceted insights into building energy end-use patterns associated with the occupant behavior. Association rule mining was employed to develop a methodology for examining all associations and correlations between building operational data, thereby discovering useful knowledge about energy conservation. The results show there are possibilities for saving energy by modifying the operation of mechanical ventilation systems and by repairing equipment. Cluster analysis, classification analysis, and association rule mining were combined to formulate a methodology for identifying and improving occupant behavior in buildings. The results show that the methodology was able to identify the behavior which needs to be modified, and provide occupants with feasible recommendations so that they can make required decisions to modify their behavior.
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Libros sobre el tema "Hidden Data Mining"

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Big data analytics with R: Utilize R to uncover hidden patterns in your big data. Birmingham, UK: Packt Publishing, 2016.

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United States. Congress. Senate. Committee on Homeland Security and Governmental Affairs. Permanent Subcommittee on Investigations. Online advertising and hidden hazards to consumer security and data privacy: Hearing before the Permanent Subcommittee on Investigations of the Committee on Homeland Security and Governmental Affairs, United States Senate, One Hundred Thirteenth Congress, second session, May 15, 2014. Washington: U.S. Government Printing Office, 2014.

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Dubner, Stephen J. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. New York, USA: Harper Torch, 2006.

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Levitt, Steven D. y Stephen J. Dubner. Freakonomics: A rogue economist explores the hidden side of everything. New York: William Morrow, 2007.

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Levitt, Steven D. y Stephen J. Dubner. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. New York, USA: William Morrow, 2006.

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Levitt, Steven D. Freakonomics: A rogue economist explores the hidden side of everything. New York: William Morrow, 2005.

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Levitt, Steven D. y Stephen J. Dubner. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. 7a ed. New York: William Morrow, 2007.

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Levitt, Steven D. Freakonomics: A rogue economist explores the hidden side of everything. New York: Harper Perennial, 2009.

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Levitt, Steven D. y Stephen J. Dubner. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. New York: William Morrow, 2005.

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Levitt, Steven D. Freakonomics: A rogue economist explores the hidden side of everything. New York: Harper Perennial, 2009.

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Capítulos de libros sobre el tema "Hidden Data Mining"

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Bosch, Antal van den. "Hidden Markov Models". En Encyclopedia of Machine Learning and Data Mining, 1–3. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_124-1.

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van den Bosch, Antal. "Hidden Markov Models". En Encyclopedia of Machine Learning and Data Mining, 609–11. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_124.

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Feng, Shi, Daling Wang, Ge Yu, Chao Yang y Nan Yang. "Chinese Blog Clustering by Hidden Sentiment Factors". En Advanced Data Mining and Applications, 140–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03348-3_16.

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Žliobaitė, Indrė. "Identifying Hidden Contexts in Classification". En Advances in Knowledge Discovery and Data Mining, 277–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20841-6_23.

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Zhou, Weida, Li Zhang y Licheng Jiao. "Hidden Space Principal Component Analysis". En Advances in Knowledge Discovery and Data Mining, 801–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11731139_93.

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Nie, Jinhui, Hongqi Su y Xiaohua Zhou. "Research on Map Matching Based on Hidden Markov Model". En Advanced Data Mining and Applications, 277–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-53914-5_24.

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Lin, Weiqiang y Mehmet A. Orgun. "Temporal Data Mining Using Hidden Periodicity Analysis". En Lecture Notes in Computer Science, 49–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39963-1_6.

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Adibi, Jafar y Wei-Min Shen. "Self-Similar Layered Hidden Markov Models". En Principles of Data Mining and Knowledge Discovery, 1–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44794-6_1.

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Yu, Jeffrey Xu. "Finding Hidden Structures in Relational Databases". En Advances in Knowledge Discovery and Data Mining, 2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_2.

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Li, Xingjuan, Yu Li y Jiangtao Cui. "Estimating Interactions of Functional Brain Connectivity by Hidden Markov Models". En Advanced Data Mining and Applications, 403–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05090-0_34.

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Actas de conferencias sobre el tema "Hidden Data Mining"

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Bhuiyan, Mansurul, Snehasis Mukhopadhyay y Mohammad Al Hasan. "Interactive pattern mining on hidden data". En the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2396777.

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Dharmavaram, Sirisha, Arshad Shaik y Wei Jin. "Mining Biomedical Data for Hidden Relationship Discovery". En 2019 IEEE International Conference on Healthcare Informatics (ICHI). IEEE, 2019. http://dx.doi.org/10.1109/ichi.2019.8904747.

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Dautriche, Remy, Alexandre Termier, Renaud Blanch y Miguel Santana. "Towards Visualizing Hidden Structures". En 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0171.

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Berti-Equille, Laure, Ji Meng Loh y Tamraparni Dasu. "A Masking Index for Quantifying Hidden Glitches". En 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.16.

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Nazi, Azade, Saravanan Thirumuruganathan, Vagelis Hristidis, Nan Zhang, Khaled Shaban y Gautam Das. "Query Hidden Attributes in Social Networks". En 2014 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE, 2014. http://dx.doi.org/10.1109/icdmw.2014.113.

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Jiang, Zhe y Arpan Man Sainju. "Hidden Markov Contour Tree". En KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3330878.

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Sato, Makoto y Shuuichiro Imahara. "Clustering Geospatial Objects via Hidden Markov Random Fields". En 2008 Eighth IEEE International Conference on Data Mining (ICDM). IEEE, 2008. http://dx.doi.org/10.1109/icdm.2008.70.

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Yoshida, Tetsuya. "Toward Finding Hidden Communities Based on User Profile". En 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2010. http://dx.doi.org/10.1109/icdmw.2010.20.

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Siraj, Fadzilah y Mansour Ali Abdoulha. "Uncovering Hidden Information Within University's Student Enrollment Data Using Data Mining". En 2009 Third Asia International Conference on Modelling & Simulation. IEEE, 2009. http://dx.doi.org/10.1109/ams.2009.117.

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Belth, Caleb, Alican Buyukcakir y Danai Koutra. "A Hidden Challenge of Link Prediction: Which Pairs to Check?" En 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00092.

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Informes sobre el tema "Hidden Data Mining"

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Bond, W., Maria Seale y Jeffrey Hensley. A dynamic hyperbolic surface model for responsive data mining. Engineer Research and Development Center (U.S.), abril de 2022. http://dx.doi.org/10.21079/11681/43886.

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Data management systems impose structure on data via a static representation schema or data structure. Information from the data is extracted by executing queries based on predefined operators. This paradigm restricts the searchability of the data to concepts and relationships that are known or assumed to exist among the objects. While this is an effective and efficient means of retrieving simple information, we propose that such a structure severely limits the ability to derive breakthrough knowledge that exists in data under the guise of “unknown unknowns.” A dynamic system will alleviate this dependence, allowing theoretically infinite projections of the data to reveal discoverable relationships that are hidden by traditional use case-driven, static query systems. In this paper, we propose a framework for a data-responsive query algebra based on a dynamic hyperbolic surface model. Such a model could provide more intuitive access to analytics and insights from massive, aggregated datasets than existing methods. This model will significantly alter the means of addressing the underlying data by representing it as an arrangement on a dynamic, hyperbolic plane. Consequently, querying the data can be viewed as a process similar to quantum annealing, in terms of characterizing data representation as an energy minimization problem with numerous minima.
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