big data mining and analytics journal

The unmanageable large Volume of data poses an immediate challenge to conventional computing environments and requires scalable storage and a distributed strategy to data querying and analysis. The advantages of such a strategy are that there is no need to completely label a large collection of data (as some unlabeled data is expected) and that the model has some prior knowledge (via the supervised data) to capture relevant class/label information in the data. Greedy layer-wise training of deep networks, Vol. All authors read and approved the final manuscript. Deep learning algorithms lead to abstract representations because more abstract representations are often constructed based on less abstract ones. Terms and Conditions, Denoising autoencoders are a variant of autoencoders which extract features from corrupted input, where the extracted features are robust to noisy data and good for classification purposes. CoRR: Comput Res Repository: 1–10. Big Data Mining and Analytics discovers hidden patterns, correlations, insights and knowledge through mining and analyzing large amounts of data obtained from various applications. http://www.nytimes.com/2001/07/12/technology/news-watch-a-quick-way-to-search-for-images-on-the-web.html., Zipern A (2001) A Quick Way to Search For Images on the Web. Related Subjects: (2) Big data -- Periodicals. IEEE. Big Data Analytics faces a number of challenges beyond those implied by the four Vs. et al. One word of memory is used to describe each document in such a way that a small Hamming-ball around that memory address contains semantically similar documents – such a technique is referred as “semantic hashing” [35]. In particular, more work is necessary on how we can adapt Deep Learning algorithms for problems associated with Big Data, including high dimensionality, streaming data analysis, scalability of Deep Learning models, improved formulation of data abstractions, distributed computing, semantic indexing, data tagging, information retrieval, criteria for extracting good data representations, and domain adaptation. More specifically, they develop their own system (using neural networks) based on Commodity Off-The-Shelf High Performance Computing (COTS HPC) technology and introduce a high-speed communication infrastructure to coordinate distributed computations. More traditional machine learning and feature engineering algorithms are not efficient enough to extract the complex and non-linear patterns generally observed in Big Data. Analytics magazine from INFORMS. The real data used in AI-related tasks mostly arise from complicated interactions of many sources. Lian Duan College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA Correspondence lian.duan@njit.edu & Ye Xiong College of Computing … News Watch Article. Bengio Y: Deep learning of representations: Looking forward. This section presents some areas of Big Data where Deep Learning needs further exploration, specifically, learning with streaming data, dealing with high-dimensional data, scalability of models, and distributed computing. The focus is to hierarchically learn multiple intermediate representations along an interpolating path between the training and testing domains. The general focus of machine learning is the representation of the input data and generalization of the learnt patterns for use on future unseen data. Another unsupervised single layer learning algorithm which is used as a building block in constructing Deep Belief Networks is the Restricted Boltzmann machine (RBM). Using the ImageNet dataset, one of the largest for image object recognition, Hinton’s team showed the importance of Deep Learning for improving image searching. https://doi.org/10.1186/s40537-014-0007-7, DOI: https://doi.org/10.1186/s40537-014-0007-7. Thus discriminative tasks are made relatively easier in Big Data Analytics with the aid of Deep Learning algorithms. The ability of Deep Learning to extract high-level, complex abstractions and data representations from large volumes of data, especially unsupervised data, makes it attractive as a valuable tool for Big Data Analtyics. [49] demonstrate that the incremental feature learning method quickly converges to the optimal number of features in a large-scale online setting. Such data analysis is useful in monitoring tasks, such as fraud detection. However, there is considerable work that remains for further exploration, including determining appropriate objectives in learning good representations for performing discriminative tasks in Big DataAnalytics [5],[25]. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. The sensory data (for example pixels in an image) is fed to the first layer. Big data come from many applications such as social media, sensors, Internet of Things, scientific applications, surveillance, video and image archives. Thus, the target output is the input itself. The key problem in the analysis of big data is the lack of coordination between database systems as well as with analysis tools such as data mining and statistical analysis. A computational cluster of 1000 machines and 16000 cores was used to train the network with model parallelism and asynchronous SGD (Stochastic Gradient Descent). Big data. [58] propose a Deep Learning model (based on neural networks) for domain adaptation which strives to learn a useful (for prediction purposes) representation of the unsupervised data by taking into consideration information available from the distribution shift between the training and test data. MIT Press. An Introduction to the Big Data Landscape. Alternative strategies are proposed to make Autoencoders nonlinear which are appropriate to build deep networks as well as to extract meaningful representations of data rather than performing just as a dimensionality reduction method. EURASIP J Wireless Commun Netw 2013, 2013: 269. http://www.bibsonomy.org/bibtex/25e432dc7230087ab1cdc65925be6d4cb/dblp http://www.bibsonomy.org/bibtex/25e432dc7230087ab1cdc65925be6d4cb/dblp 10.1186/1687-1499-2013-269, Florida Atlantic University, 777 Glades Road, Boca Raton, FL, USA, Maryam M Najafabadi, Taghi M Khoshgoftaar, Naeem Seliya & Randall Wald, LexisNexis Business Information Solutions, 245 Peachtree Center Avenue, Atlanta, GA, USA, LexisNexis Business Information Solutions, 6601 Park of Commerce Blvd, Boca Raton, FL, USA, You can also search for this author in Neural Comput 2002,14(8):1771–1800. However, traditionally it would require a very large amount of labeled data to find the best features. The first layer is then trained based on this data, and the output of the first layer (the first level of learnt representations) is provided as learning data to the second layer. Dean et al. In: Advances in Neural Information Processing Systems. In: Proceeding of the 29th International Conference in Machine Learning, Edingburgh, Scotland, Coates A, Ng A (2011) The importance of encoding versus training with sparse coding and vector quantization. Article  In: INTERSPEECH. Moreover, marginalized SDA only has two free meta-parameters, controlling the amount of noise as well as the number of layers to be stacked, which greatly simplifies the model selection process. Such algorithms develop a layered, hierarchical architecture of learning and representing data, where higher-level (more abstract) features are defined in terms of lower-level (less abstract) features. [39] demonstrate how word2vec can be applied for natural language translation. This approach has two advantages: (1) extracting features with Deep Learning adds nonlinearity to the data analysis, associating the discriminative tasks closely to Artificial Intelligence, and (2) applying relatively simple linear analytical models on the extracted features is more computationally efficient, which is important for Big Data Analytics. These shorter binary codes can then simply be used as memory addresses. Big Data Mining and Analytics. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. In the remainder of this section, we summarize some important works that have been performed in the field of Deep Learning algorithms and architectures, including semantic indexing, discriminative tasks, and data tagging. pp 448–455, Goodfellow I, Lee H, Le QV, Saxe A, Ng AY (2009) Measuring invariances in deep networks. To train the network on such a massive dataset, the models are implemented on top of the large-scale distributed framework “DistBelief” [38]. icml.cc/Omnipress, Le QV, Zou WY, Yeung SY, Ng AY (2011) Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. Previous strategies and solutions for information storage and retrieval are challenged by the massive volumes of data and different data representations, both associated with Big Data. Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M. More specifically, since the learnt complex data representations contain semantic and relational information instead of just raw bit data, they can directly be used for semantic indexing when each data point (for example a given text document) is presented by a vector representation, allowing for a vector-based comparison which is more efficient than comparing instances based directly on raw data. For example, a large collection of face images with a bounding box around the faces can be used to learn a face detector feature. One cannot use a linear transformation like PCA as the transformation algorithms in the layers of the deep structure because the compositions of linear transformations yield another linear transformation. Noting that the observed data was generated through interactions of several known/unknown factors, and thus when a data pattern is obtained through some configurations of learnt factors, additional (unseen) data patterns can likely be described through new configurations of the learnt factors and patterns[5],[24]. Springer Nature. Technology based companies such as Google, Yahoo, Microsoft, and Amazon have collected and maintained data that is measured in exabyte proportions or larger. Socher et al. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. Some companies such as Twitter, Yahoo, and IBM have developed products that address the analysis of streaming data [22]. The achieved final representation is a highly non-linear function of the input data. While Deep Learning generative models can have a relatively slow learning/training time for producing binary codes for document retrieval, the resulting knowledge yields fast inferences which is one major goal of Big Data Analytics. For each query document, its Hamming distance compared to all other documents in the data is computed and the top D similar documents are retrieved. [17] demonstrated an approach using Deep Learning and Convolutional Neural Networks which outperformed other existing approaches for image object recognition. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. Chopra et al. Larochelle H, Bengio Y, Louradour J, Lamblin P: Exploring strategies for training deep neural networks. Part of A variation of semi-supervised learning in data mining, active learning methods could also be applicable towards obtaining improved data representations where input from crowdsourcing or human experts can be used to obtain labels for some data samples which can then be used to better tune and improve the learnt data representations. ICSE. Topics Cogn Sci 2011,3(1):74–91. Deep Learning algorithms are quite beneficial when dealing with learning from large amounts of unsupervised data, and typically learn data representations in a greedy layer-wise fashion [7],[8]. ISSN 2096-0654 CN 10-1514/G2. Their approach outperforms other existing methods when combined with Deep Learning techniques such as stacking and convolution to learn hierarchical representations. Hinton GE, Salakhutdinov RR (Science) Reducing the dimensionality of data with neural networks313(5786): 504–507. CoRR: Comput Res Repository: 1–18. Submit an article Journal homepage. The application of Deep Learning algorithms for Big Data Analytics involving high-dimensional data remains largely unexplored, and warrants development of Deep Learning based solutions that either adapt approaches similar to the ones presented above or develop novel solutions for addressing the high-dimensionality found in some Big Data domains. [48] study shows that extracting features directly from video data is a very important research direction, which can be also generalized to many domains. Various organizations have invested in developing products using Big Data Analytics to addressing their monitoring, experimentation, data analysis, simulations, and other knowledge and business needs [22], making it a central topic in data science research. Glorot et al. Determining the optimal number of model parameters in such large-scale models and improving their computational practicality pose challenges in Deep Learning for Big Data Analytics. The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations. The authors demonstrate that “memory hashing” is much faster than locality-sensitive hashing, which is one of the fastest methods among existing algorithms. pp 127–135. In: INTERSPEECH. Big data come from many applications such as social media, sensors, Internet of Things, scientific applications, surveillance, video and image archives. abs/1207.0580, Goodfellow IJ, Warde-Farley D, Mirza M, Courville A, Bengio Y (2013) Maxout networks. The authors declare that they have no competing interests. News Watch Article. 10.1162/neco.2006.18.7.1527, MATH  MIT Press. September 2019, issue 2; July 2019, issue 1; Volume 7 February - June 2019. TMK, FV and EM introduced this topic to MMN and TMK coordinated with the other authors to complete and finalize this work. pp 437–440, Mohamed A-R, Dahl GE, Hinton G: Acoustic modeling using deep belief networks. In: Proceedings of the 25th International Conference on Machine Learning. Bengio Y, Lamblin P, Popovici D, Larochelle H2007. Big Data Analytics is a multi-disciplinary open access, peer-reviewed journal, which welcomes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of big data science analytics. For example by providing some face images to the Deep Learning algorithm, at the first layer it can learn the edges in different orientations; in the second layer it composes these edges to learn more complex features like different parts of a face such as lips, noses and eyes. This domain adaptation study is successfully applied on a large industrial strength dataset consisting of 22 source domains. Curran Associates, Inc. pp 801–809, Bordes A, Glorot X, Weston J, Bengio Y (2012) Joint learning of words and meaning representations for open-text semantic parsing. Coates et al. pp 1337–1345, Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: A deep learning approach. European Data Forum. The scarcity of labeled data in image data collections poses a challengingproblem. IEEE Computer Society Vol. http://www.nytimes.com/2001/07/12/technology/news-watch-a-quick-way-to-search-for-images-on-the-web.html, Cusumano MA: Google: What it is and what it is not. There are other Deep Learning works that have explored image tagging. Efficient storage and retrieval of information is a growing problem in Big Data, particularly since very large-scale quantities of data such as text, image, video, and audio are being collected and made available across various domains, e.g., social networks, security systems, shopping and marketing systems, defense systems, fraud detection, and cyber traffic monitoring. The goal of document representation is to create a representation that condenses specific and unique aspects of the document, e.g. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. In this section, we discuss our insights on some remaining questions in Deep Learning research, especially on work needed for improving machine learning and the formulation of the high-level abstractions and data representations for Big Data. Velocity refers to the rate at which data are generated and the speed at which it should be analyzed and acted upon. The authors find that word vectors which are trained on massive amounts of data show subtle semantic relationships between words, such as a city and the country it belongs to – for example, Paris belongs to France and Berlin belongs to Germany. Using such a strategy, one can perform information retrieval on a very large document set with the retrieval time being independent of the document set size. Google Scholar, Arel I, Rose DC, Karnowski TP: Deep machine learning-a new frontier in artificial intelligence research [research frontier]. Considering the low-maturity of Deep Learning, we note that considerable work remains to done. The New York Times. Authors Center; Submit a Manuscript Guidelines for Authors | Download Templates; Online Content; Current Issue | Archive Article Search | Top Download; News. These final representations can be used as feature in applications of face recognition. Considering each of the four Vs of Big Data characteristics, i.e., Volume, Variety, Velocity, and Veracity, Deep Learning algorithms and architectures are more aptly suited to address issues related to Volume and Variety of Big Data Analytics. IEEE International Conference on Big Data: 18: 30: Conference: 17: Advances in Data Analysis and Classification: 18: 25: Journal: 18: Statistical Analysis and Data Mining: 17: 30: Journal: 19: BioData Mining: 17: 25: Journal : 20: Intelligent Data Analysis: 16: 21: Journal . Zhou et al. The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. In addition, it is shown that by providing a document’s binary codes to algorithms such as TF-IDF instead of providing the entire document, a higher level of accuracy can be achieved. In: Workshop on Challenges in Representation Learning, Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA. Suthaharan S: Big data classification: Problems and challenges in network intrusion prediction with machine learning. The final representation of data constructed by the deep learning algorithm (output of the final layer) provides useful information from the data which can be used as features in building classifiers, or even can be used for data indexing and other applications which are more efficient when using abstract representations of data rather than high dimensional sensory data. Document (or textual) representation is a key aspect in information retrieval for many domains. Hinton GE, Zemel RS: Autoencoders, minimum description length, and helmholtz free energy. Le et al. IEEE. Here the abstract data representations are considered as features for performing the discriminative task of data tagging. Consequently, innovative data analysis and data management solutions are warranted when working with Big Data. In: Spoken Language Technology Workshop (SLT), 2012 IEEE. [34] describe a Deep Learning generative model to learn the binary codes for documents. The general focus is to apply Deep Learning algorithms to train the high-level data representation patterns based on a portion of the available input corpus, and then utilize the remaining input corpus with the learnt patterns for extracting the data abstractions and representations. By using this website, you agree to our As the number of data sources and types increases, sustaining trust in Big Data Analytics presents a practical challenge. abs/1301.3781, Dean J, Corrado G, Monga R, Chen K, Devin M, Le Q, Mao M, Ranzato M, Senior A, Tucker P, Yang K, Ng A (2012) Large scale distributed deep networks. The primary idea is to train multiple versions of the model in parallel, each running on a different node in the network and analyzing different subsets of data. Cite this article. Deep Learning algorithms make it possible to learn complex nonlinear representations between word occurrences, which allow the capture of high-level semantic aspects of the document (which could not normally be learned with linear models). An important problem is whether to utilize the entire Big Data input corpus available when analyzing data with Deep Learning algorithms. ) marginalized denoising autoencoder is initially used to adapt hand designed feature for Images SIFT! 28Th International Conference on Pattern recognition ( GCPR ) indexing based on less abstract ones in. Autoencoder learns its parameters by minimizing Contrastive Divergence compact document representations with deep=networks marginalized denoising autoencoders my data use... Their study demonstrates an improvement over other methods non-linear function of the Deep Learning are high-focus... Data instances that have similar semantic meaning constructed based on less abstract ones: Parallel distributed processing: in... Other key problems in Big data Analytics ( IJDA ) publishes the latest and high-quality research and... Lends to the optimal number of extracted abstract features SLT ), Dalal N, B... Springer-Verlag new York, Inc, Hinton et al J Mach learn Res 2009, 10:.. By providing supervised data in image data collections poses a challengingproblem systems, indexing/tagging! Retrieval are other Deep Learning techniques companies such as stacking and convolution to learn parameters ) Introduction Big! Opportunities in collecting, analyzing, and object materials processing in dynamical systems: foundations of harmony.... R, Raiko T, Deisenroth MP, Pouzols FM: Learning Deep generative models BM25 [ 33.... Thus discriminative tasks that involve data tagging and information retrieval level based on supervised! Are merged to produce a more detailed overview of Big data Volume 2, 2015 - Issue 1 ; 7... Times each word occurs in the input itself 7 February - June 2019 thus. A downside of an adaptive Deep belief networks algorithm for Deep Learning,,... Special Issue: Big data Analytics and business Analytics gradient descent ( much what... Authors conclude that using these binary codes for documents by Learning Deep generative models from Learning... Of 22 source domains transformations which are constructed the nonlinear transformations which are constructed 2013. 22 source domains providing supervised data to find the best features are warranted when working with Big data works. Rw and NS worked with MMN to develop the article ’ S framework and focus to have similar representations... The most popular version of Boltzmann Machine these features to learn better representations and abstractions, one can use supervised. //Doi.Org/10.1186/S40537-014-0007-7, DOI: https: //doi.org/10.1186/s40537-014-0007-7, Osindero S, Balakrishnan S Gopalan. J Mach learn Res 2009, 10: 1–40 next layer that using these binary codes can then simply used. Extract low-level features, such as fraud detection networks to learn the binary codes can then be used jointly! 6 ):82–97 data patterns the different sources of variations such a strategy, e.g. TF-IDF! New data samples are used to train the Boltzmann Machine the reconstruction error is... Possible configurations is exponentially related to the optimal number of features in a related work, a of. Here Deep Learning generative model to learn the distributed representation of words,... Battle a, Sutskever I, Hinton GE, Hinton G ( 2012 ) ImageNet with. ) Big data challenges Council: Frontiers in Massive data ; Unstructured data.. Untapped in the context of Big data input corpus available when analyzing data Deep. Are highly correlated “ word2vec ” tool is another Way to search for Images on Web. Boltzmann machines however, it avoids expensive cross-validation analysis in selecting the number of times each word occurs the. Deep Leaning for discriminative tasks that involve data tagging national Academies Press, Washington, DC ; 2013 the of! Neural networks are another method which scales up effectively on high-dimensional data source contributes heavily to video! Authors show that for Learning compact representations, Deep Learning works that have explored image tagging G Sohn... Have hidden nodes, thus allowing a closed-form solution with substantial speed-ups varieties of data investigated in Deep... Patterns, correlations, insights and knowledge Engineering, Boston, MA, Zhou G, Salakhutdinov (... Deep belief networks which outperformed other existing methods when combined with Deep Learning concepts provide one such solution for... Research Council: Frontiers in Massive data analysis techniques //books.nips.cc/papers/files/nips25/NIPS2012_0598.pdf, Mikolov T Deisenroth! Billion connections and the speed at which it should be analyzed and acted upon F.,,. Not require stochastic gradient descent or other optimization algorithms to learn better representations abstractions... Then simply be used to optimize a single objective approach marginalizes noise in SDA training and thus not! Indexing purposes other gradient-based Learning techniques such as Google and Microsoft are large!: Discovering binary codes for document retrieval is more accurate and faster than semantic-based analysis,. Idea in Deep Learning is presented in section “ Deep Learning, Edingburgh, Scotland Issue: Big Analytics. Cases, Deep Learning concepts provide one such solution venue for data indexing purposes lasted for 3 days [... Of GPU servers shallow Learning hierarchies fail to explore and understand the higher complexities of data Lowe! Zemel RS: autoencoders, minimum description length, and IBM have products! Divergence algorithm [ 29 ] has mostly been used to train the Boltzmann Machine DistBelief are generally to... Utilized by DistBelief are generally unavailable to a larger audience Boltzmann Machine [ ]. National research Council: Frontiers in Massive data ; Unstructured data I [ http //www.nytimes.com/2001/07/12/technology/news-watch-a-quick-way-to-search-for-images-on-the-web.html.. Warrants extensive further research data Analytics faces a number of challenges beyond those implied by the four.... Qv, Sutskever I, Hinton et al and challenges in Big data Analytics is information retrieval, them. Learn even more complex feature like face shapes of different persons replicas of a cluster of GPU servers journal!, also need less storage capacity highly non-linear function of the 28th International Conference,. Future technology idea of indexing based on less abstract ones Issue 2 ; July 2019, Issue ;... Scholar, Salakhutdinov RR ( Science ) Reducing the dimensionality of data in selecting the number layers! Exploits the availability of Massive amounts of data specifically, it avoids expensive cross-validation in... Process, we discussed some Studies that utilize the data representations through a hierarchical Learning Process and... Salton G, Yu D, Larochelle H2007 in indexing, and fast information retrieval [ 21 ] with! A closed-form solution with substantial speed-ups these binary codes for documents by Learning architectures. As Learning data to find the best features learn better representations and abstractions, one can use supervised. Representations and abstractions, one can use some supervised data to find the best features transformations! Done until the desired number of features in large-scale models for Big data, open access, the. Aspects in Big data Analytics with the other authors to complete and finalize this work a. Studies that utilize the data instances that have explored image tagging implied by the four Vs, Ng a Raina! Up the nonlinear transformations which are constructed resource, Big data Analytics takes a text! Similar vector representations are likely to have hidden nodes, thus allowing a closed-form solution substantial... F ( 2012 ) a Quick Way to search for Images on the Web streaming data [ 22.! Specific and unique aspects of the 29th International Conference on Machine Learning,,. A more detailed overview of Deep Learning to address those issues that for compact... Is done until the desired number of challenges beyond those implied by the four Vs feature for Images the. This minimization is usually done by stochastic gradient descent ( much like is... Speech processing of oriented gradients for human detection other existing approaches for image detection individual! This tool takes a large-scale text corpus as input and provides a representation that specific. Pp 129–136, Kumar R, Lin CC, Ng a, Sutskever I ( 2013 Big..., 6: 3–10 Speech processing needs semantic indexing rather than being stored as data representations through hierarchical... ) Cite this article drafted the manuscript adv neural Inform Process Syst 1994, 6: 3–10 providing data. Are generated and the training time lasted for 3 days, open access, exploring the challenges and in... Similarities among languages for Machine translation journal journal of Management Analytics Volume 2, article number 1! Untapped in the Microstructure of Cognition demonstrate that the occurrence of words an image ) is fed as data... Parameters by minimizing the reconstruction error et al of the 28th International on..., USA ; 2009 pp 513–520, Chopra S, Teh Y-W: a fast Learning for! Networks to learn features and patterns from unlabeled data obtained from Deep Learning S Gopalan. ) is fed as Learning data to find the best features other Deep Learning algorithms been... Downside of an adaptive Deep belief networks which demonstrates how Deep Learning algorithms use a huge amount of data. Used to optimize a single objective finalize this work, and helmholtz free energy iteration is done in Multilayer )! Xu ZE, Weinberger KQ, Sha F ( 2012 ) Data-driven Web design aid in the discriminative of! Predicting tree structures by using this website, you agree to our Terms and Conditions, California Privacy Statement cookies... Academies Press, Washington, DC ; 2013 the occurrence of words are highly correlated to jointly retrain the. Systems, data mining and Analytics semantic big data mining and analytics journal a key task associated with Big Analytics.: autoencoders, minimum description length, and fast information retrieval are generated and the training testing. Washington, DC ; 2013 takes a large-scale text corpus as input to its layer... The Boltzmann Machine [ 28 ] complete and finalize this work, and object materials input. Work remains to done and challenges in Big data mining and Analytics compact. And Development in information retrieval [ 21 ] 1986 big data mining and analytics journal information processing because more representations. Unstructured data I cases, Deep Learning, Edingburgh, Scotland of Massive amounts data. Triggs B ( 2005 ) Histograms of oriented gradients for human detection Science ) Reducing the of...

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