Relevant bibliographies by topics / 080109 Pattern Recognition and Data Mining

Academic literature on the topic '080109 Pattern Recognition and Data Mining'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "080109 Pattern Recognition and Data Mining"

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Last, M. "Pattern Recognition Algorithms for Data Mining." Journal of the American Statistical Association 102, no. 478 (June 2007): 759. http://dx.doi.org/10.1198/jasa.2007.s186.

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Bauckhage, Christian, and Kristian Kersting. "Data Mining and Pattern Recognition in Agriculture." KI - Künstliche Intelligenz 27, no. 4 (August 7, 2013): 313–24. http://dx.doi.org/10.1007/s13218-013-0273-0.

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ZHAO, Xiao-yan, and Zhao-hui ZHANG. "Flatness defect pattern recognition with data mining technology." Journal of Computer Applications 29, no. 3 (May 6, 2009): 795–97. http://dx.doi.org/10.3724/sp.j.1087.2009.00795.

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Zhang, Xuelong. "Research on Data Mining Algorithm Based on Pattern Recognition." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 06 (October 4, 2019): 2059015. http://dx.doi.org/10.1142/s0218001420590156.

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With the advent of the era of big data, people are eager to extract valuable knowledge from the rapidly expanding data, so that they can more effectively use these massive storage data. The traditional data processing technology can only achieve basic functions such as data query and statistics, and cannot achieve the goal of extracting the knowledge existing in the data to predict the future trend. Therefore, along with the rapid development of database technology and the rapid improvement of computer’s computing power, data mining (DM) came into existence. Research on DM algorithms includes knowledge of various fields such as database, statistics, pattern recognition and artificial intelligence. Pattern recognition mainly extracts features of known data samples. The DM algorithm using pattern recognition technology is a better method to obtain effective information from massive data, thus providing decision support, and has a good application prospect. Support vector machine (SVM) is a new pattern recognition algorithm proposed in recent years, which avoids dimension disaster by dimensioning and linearization. Based on this, this paper studies the DM algorithm based on pattern recognition, and proposes a DM algorithm based on SVM. The algorithm divides the vector of the SV set into two different types and iterates through multiple iterations to obtain a classifier that converges to the final result. Finally, through the cross-validation simulation experiment, the results show that the DM algorithm based on pattern recognition can effectively reduce the training time and solve the mining problem of massive data. The results show that the algorithm has certain rationality and feasibility.
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Armengol, Eva, Dionís Boixader, and Francisco Grimaldo. "Special Issue on Pattern Recognition Techniques in Data Mining." Pattern Recognition Letters 93 (July 2017): 1–2. http://dx.doi.org/10.1016/j.patrec.2017.02.014.

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Fan, Xue Dong. "Clustering Analysis Based on Data Mining Applications." Applied Mechanics and Materials 303-306 (February 2013): 1026–29. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1026.

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Abstract. In this paper, a clustering algorithm based on data mining technology applications, the use of the extraction mode noise characteristics amount and pattern recognition algorithms, extraction and selection of the characteristic quantities of the three types of mode, carried out under the same working conditions data mining clustering analysis ultimately satisfying recognition.
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Calvetti, Daniela. "Book Review: Matrix methods in data mining and pattern recognition." Mathematics of Computation 78, no. 267 (September 1, 2009): 1867–68. http://dx.doi.org/10.1090/s0025-5718-09-02247-9.

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Ingle, Dr D. R., and Mr Samirkumar R. Waghmare. "Analysis of Big Data Methodology for Pattern Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 530–35. http://dx.doi.org/10.22214/ijraset.2022.43806.

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Abstract: Sentiment Analysis (SA) is an ongoing field of research in text mining field. SA is the computational treatment of opinions, sentiments and subjectivity of text. This survey paper tackles a comprehensive overview of the last update in this field. Many recently proposed algorithms' enhancements and various SA applications are investigated and presented briefly in this survey. These articles are categorized according to their contributions in the various SA techniques. The related fields to SA (transfer learning, emotion detection, and building resources) that attracted researchers recently are discussed.
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Sun, Lu, Wei Zhou, Jian Guan, and You He. "Mining spatial–temporal motion pattern for vessel recognition." International Journal of Distributed Sensor Networks 14, no. 5 (May 2018): 155014771877956. http://dx.doi.org/10.1177/1550147718779563.

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Approaches of vessel recognition are mostly accomplished by sensing targets and extracting target features, without taking advantage of spatial and temporal motion features. With maritime situation management systems widely applied, vessels’ spatial and temporal state information can be obtained by many kinds of distributed sensors, which is easy to achieve long-time accumulation but are often forgotten in databases. In order to get valuable information from large-scale stored trajectories for unknown vessel recognition, a spatial and temporal constrained trajectory similarity model and a mining algorithm based on spatial and temporal constrained trajectory similarity are proposed in this article by searching trajectories with similar motion features. Based on the idea of finding matching points between trajectories, baseline matching points are first defined to provide time reference for trajectories at different time, then the almost matching points are obtained by setting the spatial and temporal constraints, and the similarity of pairwise almost matching points is defined, which derives the spatial and temporal similarity of trajectories. By searching the matching points from trajectories, the similar motion pattern is extracted. Experiments on real data sets show that the proposed algorithm is useful for similar moving behavior mining from historic trajectories, which can strengthen motion feature with the length increases, and the support for vessel with unknown property is larger than other models.
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Zhihao, LIANG, WU Jianghong, and XIE Zili. "Variable Frequency Room Air Conditioner Operation Pattern Recognition and Data Mining." Journal of Mechanical Engineering 55, no. 6 (2019): 194. http://dx.doi.org/10.3901/jme.2019.06.194.

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Dissertations / Theses on the topic "080109 Pattern Recognition and Data Mining"

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Seevinck, Jennifer. "Emergence in interactive art." Thesis, University of Technology, Sydney, 2011.

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This thesis is concerned with creating and evaluating interactive art systems that facilitate emergent participant experiences. For the purposes of this research, interactive art is the computer based arts involving physical participation from the audience, while emergence is when a new form or concept appears that was not directly implied by the context from which it arose. This emergent ‘whole’ is more than a simple sum of its parts. The research aims to develop understanding of the nature of emergent experiences that might arise during participant interaction with interactive art systems. It also aims to understand the design issues surrounding the creation of these systems. The approach used is Practice-based, integrating practice, evaluation and theoretical research. Practice used methods from Reflection-in-action and Iterative design to create two interactive art systems: Glass Pond and +-now. Creation of +-now resulted in a novel method for instantiating emergent shapes. Both art works were also evaluated in exploratory studies. In addition, a main study with 30 participants was conducted on participant interaction with +-now. These sessions were video recorded and participants were interviewed about their experience. Recordings were transcribed and analysed using Grounded theory methods. Emergent participant experiences were identified and classified using a taxonomy of emergence in interactive art. This taxonomy draws on theoretical research. The outcomes of this Practice-based research are summarised as follows. Two interactive art systems, where the second work clearly facilitates emergent interaction, were created. Their creation involved the development of a novel method for instantiating emergent shapes and it informed aesthetic and design issues surrounding interactive art systems for emergence. A taxonomy of emergence in interactive art was also created. Other outcomes are the evaluation findings about participant experiences, including different types of emergence experienced and the coding schemes produced during data analysis.
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Nguyen, Thuy Thi Thu. "Predicting cardiovascular risks using pattern recognition and data mining." Thesis, University of Hull, 2009. http://hydra.hull.ac.uk/resources/hull:3051.

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This thesis presents the use of pattern recognition and data mining techniques into risk prediction models in the clinical domain of cardiovascular medicine. The data is modelled and classified by using a number of alternative pattern recognition and data mining techniques in both supervised and unsupervised learning methods. Specific investigated techniques include multilayer perceptrons, radial basis functions, and support vector machines for supervised classification, and self organizing maps, KMIX and WKMIX algorithms for unsupervised clustering. The Physiological and Operative Severity Score for enUmeration of Mortality and morbidity (POSSUM), and Portsmouth POSSUM (PPOSSUM) are introduced as the risk scoring systems used in British surgery, which provide a tool for predicting risk adjustment and comparative audit. These systems could not detect all possible interactions between predictor variables whereas these may be possible through the use of pattern recognition techniques. The thesis presents KMIX and WKMIX as an improvement of the K-means algorithm; both use Euclidean and Hamming distances to measure the dissimilarity between patterns and their centres. The WKMIX is improved over the KMIX algorithm, and utilises attribute weights derived from mutual information values calculated based on a combination of Baye’s theorem, the entropy, and Kullback Leibler divergence. The research in this thesis suggests that a decision support system, for cardiovascular medicine, can be built utilising the studied risk prediction models and pattern recognition techniques. The same may be true for other medical domains.
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Kou, Yufeng. "Abnormal Pattern Recognition in Spatial Data." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30145.

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In the recent years, abnormal spatial pattern recognition has received a great deal of attention from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden but valuable knowledge in many applications. For example, it can help locate extreme meteorological events such as tornadoes and hurricanes, identify aberrant genes or tumor cells, discover highway traffic congestion points, pinpoint military targets in satellite images, determine possible locations of oil reservoirs, and detect water pollution incidents. Numerous traditional outlier detection methods have been developed, but they cannot be directly applied to spatial data in order to extract abnormal patterns. Traditional outlier detection mainly focuses on "global comparison" and identifies deviations from the remainder of the entire data set. In contrast, spatial outlier detection concentrates on discovering neighborhood instabilities that break the spatial continuity. In recent years, a number of techniques have been proposed for spatial outlier detection. However, they have the following limitations. First, most of them focus primarily on single-attribute outlier detection. Second, they may not accurately locate outliers when multiple outliers exist in a cluster and correlate with each other. Third, the existing algorithms tend to abstract spatial objects as isolated points and do not consider their geometrical and topological properties, which may lead to inexact results. This dissertation reports a study of the problem of abnormal spatial pattern recognition, and proposes a suite of novel algorithms. Contributions include: (1) formal definitions of various spatial outliers, including single-attribute outliers, multi-attribute outliers, and region outliers; (2) a set of algorithms for the accurate detection of single-attribute spatial outliers; (3) a systematic approach to identifying and tracking region outliers in continuous meteorological data sequences; (4) a novel Mahalanobis-distance-based algorithm to detect outliers with multiple attributes; (5) a set of graph-based algorithms to identify point outliers and region outliers; and (6) extensive analysis of experiments on several spatial data sets (e.g., West Nile virus data and NOAA meteorological data) to evaluate the effectiveness and efficiency of the proposed algorithms.
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Gawande, Rashmi. "Evaluation of Automotive Data mining and Pattern Recognition Techniques for Bug Analysis." Master's thesis, Universitätsbibliothek Chemnitz, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-196770.

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In an automotive infotainment system, while analyzing bug reports, developers have to spend significant time on reading log messages and trying to locate anomalous behavior before identifying its root cause. The log messages need to be viewed in a Traceviewer tool to read in a human readable form and have to be extracted to text files by applying manual filters in order to further analyze the behavior. There is a need to evaluate machine learning/data mining methods which could potentially assist in error analysis. One such method could be learning patterns for “normal” messages. “Normal” could even mean that they contain keywords like “exception”, “error”, “failed” but are harmless or not relevant to the bug that is currently analyzed. These patterns could then be applied as a filter, leaving behind only truly anomalous messages that are interesting for analysis. A successful application of the filter would reduce the noise, leaving only a few “anomalous” messages. After evaluation of the researched candidate algorithms, two algorithms namely GSP and FP Growth were found useful and thus implemented together in a prototype. The prototype implementation overall includes processes like pre-processing, creation of input, executing algorithms, creation of training set and analysis of new trace logs. Execution of prototype resulted in reducing manual effort thus achieving the objective of this thesis work.
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Liu, Guimei. "Supporting efficient and scalable frequent pattern mining /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?COMP%202005%20LIUG.

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Wu, Jianfei. "Vector-Item Pattern Mining Algorithms and their Applications." Diss., North Dakota State University, 2011. https://hdl.handle.net/10365/28841.

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Advances in storage technology have long been driving the need for new data mining techniques. Not only are typical data sets becoming larger, but the diversity of available attributes is increasing in many problem domains. In biological applications for example, a single protein may have associated sequence-, text-, graph-, continuous and item data. Correspondingly, there is growing need for techniques to find patterns in such complex data. Many techniques exist for mapping specific types of data to vector space representations, such as the bag-of-words model for text [58] or embedding in vector spaces of graphs [94, 91]. However, there are few techniques that recognize the resulting vector space representations as units that may be combined and further processed. This research aims to mine important vector-item patterns hidden across multiple and diverse data sources. We consider sets of related continuous attributes as vector data and search for patterns that relate a vector attribute to one or more items. The presence of an item set defines a subset of vectors that may or may not show unexpected density fluctuations. Two types of vector-item pattern mining algorithms have been developed, namely histogram-based vector-item pattern mining algorithms and point distribution vector-item pattern mining algorithms. In histogram-based vector-item pattern mining algorithms, a vector-item pattern is significant or important if its density histogram significantly differs from what is expected for a random subset of transactions, using ?? goodness-of-fit test or effect size analysis. For point distribution vector-item pattern mining algorithms, a vector-item pattern is significant if its probability density function (PDF) has a big KullbackLeibler divergence from random subsamples. We have applied the vector-item pattern mining algorithms to several application areas, and by comparing with other state-of-art algorithms we justify the effectiveness and efficiency of the algorithms.
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Ke, Yiping. "Efficient correlated pattern discovery in databases /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20KE.

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Leighty, Brian David. "Data Mining for Induction of Adjacency Grammars and Application to Terrain Pattern Recognition." NSUWorks, 2009. http://nsuworks.nova.edu/gscis_etd/212.

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The process of syntactic pattern recognition makes the analogy between the syntax of languages and the structure of spatial patterns. The recognition process is achieved by parsing a given pattern to determine if it is syntactically correct with respect to a defined grammar. The generation of pattern grammars can be a cumbersome process when many objects are involved. This has led to the problem of spatial grammar inference. Current approaches have used genetic algorithms and inductive techniques and have demonstrated limitations. Alternative approaches are needed that produce accurate grammars while remaining computationally efficient in light of the NP-hardness of the problem. Co-location rule mining techniques in the field of Knowledge Discovery and Data Mining address the complexity issue using neighborhood restrictions and pruning strategies based on monotonic Measures Of Interest. The goal of this research was to develop and evaluate an inductive method for inferring an adjacency grammar utilizing co-location rule mining techniques to gain efficiency while providing accurate and concise production sets. The method incrementally discovers, without supervision, adjacency patterns in spatial samples, relabels them via a production rule and repeats the procedure with the newly labeled regions. The resulting rules are used to form an adjacency grammar. Grammars were generated and evaluated within the context of a syntactic pattern recognition system that identifies landform patterns in terrain elevation datasets. The proposed method was tested using a k-fold cross-validation methodology. Two variations were also tested using unsupervised and supervised training, both with no rule pruning. Comparison of these variations with the proposed method demonstrated the effectiveness of rule pruning and rule discovery. Results showed that the proposed method of rule inference produced rulesets having recall, precision and accuracy values of 82.6%, 97.7% and 92.8%, respectively, which are similar to those using supervised training. These rulesets were also the smallest, had the lowest average number of rules fired in parsing, and had the shortest average parse time. The use of rule pruning substantially reduced rule inference time (104.4 s vs. 208.9 s). The neighborhood restriction used in adjacency calculations demonstrated linear complexity in the number of regions.
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Loekito, Elsa. "Mining simple and complex patterns efficiently using binary decision diagrams /." Connect to thesis, 2009. http://repository.unimelb.edu.au/10187/4378.

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Freeman, Dane Fletcher. "A product family design methodology employing pattern recognition." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50267.

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Sharing components in a product family requires a trade-off between the individual products' performances and overall family costs. It is critical for a successful family to identify which components are similar, so that sharing does not compromise the individual products' performances. This research formulates two commonality identification approaches for use in product family design and investigates their applicability in a generic product family design methodology. Having a commonality identification approach reduces the combinatorial sharing problem and allows for more quality family alternatives to be considered. The first is based on the pattern recognition technique of fuzzy c-means clustering in component subspaces. If components from different products are similar enough to be grouped into the same cluster, then those components could possibly become the same platform. Fuzzy equivalence relations that show the binary relationship from one products' component to a different products' component can be extracted from the cluster membership functions. The second approach builds a Bayesian network representing the joint distribution of a design space exploration. Using this model, a series of inferences can be made based on product performance and component constraints. Finally the posterior design variable distributions can be processed using a similarity metric like the earth mover distance to identify which products' components are similar to another's.
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Books on the topic "080109 Pattern Recognition and Data Mining"

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Singh, Sameer, Maneesha Singh, Chid Apte, and Petra Perner, eds. Pattern Recognition and Data Mining. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188.

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Cakmakov, Dusan. Feature selection for pattern recognition. Skopje: Informa, 2002.

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Perner, Petra, and Maria Petrou, eds. Machine Learning and Data Mining in Pattern Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48097-8.

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Perner, Petra, ed. Machine Learning and Data Mining in Pattern Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73499-4.

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Perner, Petra, ed. Machine Learning and Data Mining in Pattern Recognition. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21024-7.

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Perner, Petra, ed. Machine Learning and Data Mining in Pattern Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03070-3.

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Perner, Petra, ed. Machine Learning and Data Mining in Pattern Recognition. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96133-0.

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Perner, Petra, ed. Machine Learning and Data Mining in Pattern Recognition. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96136-1.

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Perner, Petra, ed. Machine Learning and Data Mining in Pattern Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31537-4.

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Perner, Petra, ed. Machine Learning and Data Mining in Pattern Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23199-5.

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Book chapters on the topic "080109 Pattern Recognition and Data Mining"

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Rocha, Juan C., and Stefan Daume. "Data mining and pattern recognition." In The Routledge Handbook of Research Methods for Social-Ecological Systems, 241–51. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003021339-21.

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Singh, Vineeta, and Vandana Dixit Kaushik. "Concepts of Data Mining and Process Mining." In Process Mining Techniques for Pattern Recognition, 1–17. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003169550-1.

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Zanibbi, Richard, Dorothea Blostein, and James R. Cordy. "Recognition Tasks Are Imitation Games." In Pattern Recognition and Data Mining, 209–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_23.

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Cyganek, Bogusław, and Michał Woźniak. "Efficient Multidimensional Pattern Recognition in Kernel Tensor Subspaces." In Data Mining and Big Data, 529–37. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40973-3_54.

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Badr, Ghada, and B. John Oommen. "Enhancing Trie-Based Syntactic Pattern Recognition Using AI Heuristic Search Strategies." In Pattern Recognition and Data Mining, 1–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_1.

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Moreno, David L., Carlos V. Regueiro, Roberto Iglesias, and Senén Barro. "Making Use of Unelaborated Advice to Improve Reinforcement Learning: A Mobile Robotics Approach." In Pattern Recognition and Data Mining, 89–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_10.

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Pérez, Jesús M., Javier Muguerza, Olatz Arbelaitz, Ibai Gurrutxaga, and José I. Martín. "Consolidated Trees: Classifiers with Stable Explanation. A Model to Achieve the Desired Stability in Explanation." In Pattern Recognition and Data Mining, 99–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_11.

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Lane, Peter C. R., and Fernand Gobet. "Discovering Predictive Variables When Evolving Cognitive Models." In Pattern Recognition and Data Mining, 108–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_12.

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Ortiz, F., S. Puente, and F. Torres. "Mathematical Morphology and Binary Geodesy for Robot Navigation Planning." In Pattern Recognition and Data Mining, 118–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_13.

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Silva, Luís M., Luís A. Alexandre, and J. Marques de Sá. "Neural Network Classification: Maximizing Zero-Error Density." In Pattern Recognition and Data Mining, 127–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_14.

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Conference papers on the topic "080109 Pattern Recognition and Data Mining"

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Yang, Jie, Ying Hu, and Guozheng Li. "Target recognition and tracking based on data fusion and data mining." In Multispectral Image Processing and Pattern Recognition, edited by Deren Li, Jie Yang, Jufu Feng, and Shen Wei. SPIE, 2001. http://dx.doi.org/10.1117/12.440293.

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Bethel, C. L., L. O. Hall, and D. Goldgof. "Mining for Implications in Medical Data." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.800.

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Zhiwen Yu and Hau-San Wong. "Mining Uncertain Data in Low-dimensional Subspace." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.801.

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Vilches, E., I. A. Escobar, E. E. Vallejo, and C. E. Taylor. "Data Mining Applied to Acoustic Bird Species Recognition." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.426.

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PAL, SANKAR K. "SOFT COMPUTING PATTERN RECOGNITION, CASE GENERATION AND DATA MINING." In Proceedings of the Second International Conference. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704313_0003.

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Cantoni, V., L. Lombardi, and P. Lombardi. "Challenges for Data Mining in Distributed Sensor Networks." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.359.

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Chen, Xiangtao, and Ziping Guan. "Mining strong jumping emerging patterns with a novel list data structure." In Second International Workshop on Pattern Recognition, edited by Xudong Jiang, Masayuki Arai, and Guojian Chen. SPIE, 2017. http://dx.doi.org/10.1117/12.2280297.

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Suriyakumari, V., and A. V. Kathiravan. "An ubiquitous domain Driven Data Mining approach for performance monitoring in virtual organizations using 360 Degree data mining & opinion mining." In 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering (PRIME). IEEE, 2013. http://dx.doi.org/10.1109/icprime.2013.6496491.

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Yao, Lixiu, Chenzhou Ye, and Jie Yang. "Using multiple data mining techniques to assist MRI diagnosis of brain glioma." In Multispectral Image Processing and Pattern Recognition, edited by Deren Li, Jie Yang, Jufu Feng, and Shen Wei. SPIE, 2001. http://dx.doi.org/10.1117/12.440300.

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De Alwis, Buddhika, Supun Malinga, Kathiravelu Pradeeban, Denis Weerasiri, and Shehan Perera. "Horizontal format data mining with extended bitmaps." In 2010 International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2010. http://dx.doi.org/10.1109/socpar.2010.5686156.

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Reports on the topic "080109 Pattern Recognition and Data Mining"

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Kamath, C., and R. Musick. Scalable pattern recognition for large-scale scientific data mining. Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/310913.

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Baldwin, C., C. Kamath, and R. Musick. An LLNL perspective on ASCI data mining and pattern recognition requirements. Office of Scientific and Technical Information (OSTI), January 1999. http://dx.doi.org/10.2172/9659.

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Kamath, C. LDRD 99-ERI-010 Final Report: Sapphire: Scalable Pattern Recognition for Large-Scale Scientific Data Mining. Office of Scientific and Technical Information (OSTI), January 2002. http://dx.doi.org/10.2172/15003138.

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