Relevant bibliographies by topics / 080109 Pattern Recognition and Data Mining / Journal articles

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

To see the other types of publications on this topic, follow the link: 080109 Pattern Recognition and Data Mining.
Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic '080109 Pattern Recognition and Data Mining.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
11

Zhang, Junlin, Samuel Oluwarotimi Williams, and Haoxiang Wang. "Intelligent computing system based on pattern recognition and data mining algorithms." Sustainable Computing: Informatics and Systems 20 (December 2018): 192–202. http://dx.doi.org/10.1016/j.suscom.2017.10.010.

Full text
APA, Harvard, Vancouver, ISO, and other styles
12

Hand, David J. "Matrix Methods in Data Mining and Pattern Recognition by Lars Eldén." International Statistical Review 75, no. 3 (December 10, 2007): 418. http://dx.doi.org/10.1111/j.1751-5823.2007.00030_10.x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
13

Yang, Jie, Chenzhou Ye, and Nianyi Chen. "DMiner-I: A software tool of data mining and its applications." Robotica 20, no. 5 (September 2002): 499–508. http://dx.doi.org/10.1017/s0263574702004307.

Full text
Abstract:
SummaryA software tool for data mining (DMiner-I) is introduced, which integrates pattern recognition (PCA, Fisher, clustering, HyperEnvelop, regression), artificial intelligence (knowledge representation, decision trees), statistical learning (rough set, support vector machine), and computational intelligence (neural network, genetic algorithm, fuzzy systems). It consists of nine function models: pattern recognition, decision trees, association rule, fuzzy rule, neural network, genetic algorithm, HyperEnvelop, support vector machine and visualization. The principle, algorithms and knowledge representation of some function models of data mining are described. Nonmonotony in data mining is dealt with by concept hierarchy and layered mining. The software tool of data mining is realized byVisual C++under Windows 2000. The software tool of data mining has been satisfactorily applied in the prediction of regularities of the formation of ternary intermetallic compounds in alloy systems, and diagnosis of brain glioma.
APA, Harvard, Vancouver, ISO, and other styles
14

Trafalis, Theodore B., and Anderson White. "Data Mining Techniques for Pattern Recognition: Tornado Signatures in Doppler Weather Radar Data." International Journal of Smart Engineering System Design 5, no. 4 (October 2003): 347–59. http://dx.doi.org/10.1080/10255810390224107.

Full text
APA, Harvard, Vancouver, ISO, and other styles
15

Eldén, Lars. "Numerical linear algebra in data mining." Acta Numerica 15 (May 2006): 327–84. http://dx.doi.org/10.1017/s0962492906240017.

Full text
Abstract:
Ideas and algorithms from numerical linear algebra are important in several areas of data mining. We give an overview of linear algebra methods in text mining (information retrieval), pattern recognition (classification of handwritten digits), and PageRank computations for web search engines. The emphasis is on rank reduction as a method of extracting information from a data matrix, low-rank approximation of matrices using the singular value decomposition and clustering, and on eigenvalue methods for network analysis.
APA, Harvard, Vancouver, ISO, and other styles
16

Dhivya, S., and Dr R. Shanmugavadivu. "A Big Data Based Edge Detection Method for Image Pattern Recognition - A Survey." International Journal Of Engineering And Computer Science 7, no. 03 (March 22, 2018): 23755–60. http://dx.doi.org/10.18535/ijecs/v7i3.17.

Full text
Abstract:
In Today’s era Big Data is one of the most well-known research area that try to solve many research problems. The focus is mainly on how to come out those problems of Big Data and it could be handling in recent systems. Image mining and genetic algorithm is used to automate the process of images, patterns, data sets and etc. Image mining is used to extract the hidden images from the set of images. Genetic algorithm is also quite effective in solving certain optimization and intelligence problems and it is used in many applications, including image pattern recognition. The survey paper reviews of Big Data with edge detection methods on various types of images. In edge detection image pattern recognition is to choose the best images from the group of images by using both image mining and genetic algorithm techniques
APA, Harvard, Vancouver, ISO, and other styles
17

Hou, Zeng-Guang, Marios M. Polycarpou, and Haibo He. "Editorial to Special Issue: Neural networks for pattern recognition and data mining." Soft Computing 12, no. 7 (October 10, 2007): 613–14. http://dx.doi.org/10.1007/s00500-007-0254-z.

Full text
APA, Harvard, Vancouver, ISO, and other styles
18

Pastuchová, Elena, and Štefánia Václavíková. "Cluster Analysis – Data Mining Technique for Discovering Natural Groupings in the Data." Journal of Electrical Engineering 64, no. 2 (March 1, 2013): 128–31. http://dx.doi.org/10.2478/jee-2013-0019.

Full text
Abstract:
Amount of data stored in databases has been growing rapidly. With the technology of pattern recognition and statistical and mathematical techniques sieved across the stored information, data mining helps researchers recognize important facts, relationships, trends, patterns, derogations and anomalies that might otherwise go undetected. One of the major data mining techniques is clustering In this paper some of clustering methods, helpful in many applications, are compared. We assess the suitability of the software that we used for clustering.
APA, Harvard, Vancouver, ISO, and other styles
19

Brito, Paula, and Donato Malerba. "Mining official data." Intelligent Data Analysis 7, no. 6 (December 16, 2003): 497–500. http://dx.doi.org/10.3233/ida-2003-7601.

Full text
APA, Harvard, Vancouver, ISO, and other styles
20

Khoshahval, S., M. Farnaghi, and M. Taleai. "SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 395–99. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-395-2017.

Full text
Abstract:
Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.
APA, Harvard, Vancouver, ISO, and other styles
21

Singh, Lokendra. "Data Mining: Review, Drifts and Issues." International Journal of Advance Research and Innovation 1, no. 2 (2013): 20–24. http://dx.doi.org/10.51976/ijari.121305.

Full text
Abstract:
This paper gives a good overview of Data and Information or Knowledge has a significant role on human activities. Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information. Due to the importance of extracting knowledge/information from the large data repositories, data mining has become an essential component in various fields of human life. Advancements in Statistics, Machine Learning, Artificial Intelligence, Pattern Recognition and Computation capabilities have evolved the present day’s data mining applications and these applications have enriched the various fields of human life including business, education, medical, scientific etc. Hence, this paper discusses the various improvements in the field of data mining from past to the present and explores the future trends
APA, Harvard, Vancouver, ISO, and other styles
22

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

Full text
Abstract:
With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, and so on) has been collected in research laboratories in various scientific disciplines, particularly in cognitive and behavioral studies. How to automatically and effectively discover new knowledge from rich multimedia data poses a compelling challenge because most state-of-the-art data mining techniques can only search and extract pre-defined patterns or knowledge from complex heterogeneous data. In light of this challenge, we propose a hybrid approach that allows scientists to use data mining as a first pass, and then forms a closed loop of visual analysis of current results followed by more data mining work inspired by visualization, the results of which can be in turn visualized and lead to the next round of visual exploration and analysis. In this way, new insights and hypotheses gleaned from the raw data and the current level of analysis can contribute to further analysis. As a first step toward this goal, we implement a visualization system with three critical components: (1) a smooth interface between visualization and data mining; (2) a flexible tool to explore and query temporal data derived from raw multimedia data; and (3) a seamless interface between raw multimedia data and derived data. We have developed various ways to visualize both temporal correlations and statistics of multiple derived variables as well as conditional and high-order statistics. Our visualization tool allows users to explore, compare and analyze multi-stream derived variables and simultaneously switch to access raw multimedia data.
APA, Harvard, Vancouver, ISO, and other styles
23

Muszyński, Michał, and Stanisław Osowski. "Data mining methods for gene selection on the basis of gene expression arrays." International Journal of Applied Mathematics and Computer Science 24, no. 3 (September 1, 2014): 657–68. http://dx.doi.org/10.2478/amcs-2014-0048.

Full text
Abstract:
Abstract The paper presents data mining methods applied to gene selection for recognition of a particular type of prostate cancer on the basis of gene expression arrays. Several chosen methods of gene selection, including the Fisher method, correlation of gene with a class, application of the support vector machine and statistical hypotheses, are compared on the basis of clustering measures. The results of applying these individual selection methods are combined together to identify the most often selected genes forming the required pattern, best associated with the cancerous cases. This resulting pattern of selected gene lists is treated as the input data to the classifier, performing the task of the final recognition of the patterns. The numerical results of the recognition of prostate cancer from normal (reference) cases using the selected genes and the support vector machine confirm the good performance of the proposed gene selection approach
APA, Harvard, Vancouver, ISO, and other styles
24

Fang, Fu Gui. "The Research of Data Mining Based on Fuzzy Cluster." Applied Mechanics and Materials 88-89 (August 2011): 763–66. http://dx.doi.org/10.4028/www.scientific.net/amm.88-89.763.

Full text
Abstract:
Fuzzy clustering analysis is an important branch of unsupervised pattern recognition. Studying the algorithm and applications of fuzzy clustering is of great significance. This paper introduces the basic knowledge of fuzzy set theory, including the definition of the fuzzy set, its theorem fuzzy relation and so on firstly. Then this paper describes how to use fuzzy clustering analysis method for data classification and the steps of fuzzy clustering analysis.
APA, Harvard, Vancouver, ISO, and other styles
25

Mukhlash, Imam, Desna Yuanda, and Mohammad Iqbal. "Mining Fuzzy Time Interval Periodic Patterns in Smart Home Data." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (October 1, 2018): 3374. http://dx.doi.org/10.11591/ijece.v8i5.pp3374-3385.

Full text
Abstract:
A convergence of technologies in data mining, machine learning, and a persuasive computer has led to an interest in the development of smart environment to help human with functions, such as monitoring and remote health interventions, activity recognition, energy saving. The need for technology development was confirmed again by the aging population and the importance of individual independent in their own homes. Pattern mining on sensor data from smart home is widely applied in research such as using data mining. In this paper, we proposed a periodic pattern mining in smart house data that is integrated between the FP-Growth PrefixSpan algorithm and a fuzzy approach, which is called as fuzzy-time interval periodic patterns mining. Our purpose is to obtain the periodic pattern of activity at various time intervals. The simulation results show that the resident activities can be recognized by analyzing the triggered sensor patterns, and the impacts of minimum support values to the number of fuzzy-time-interval periodic patterns generated. Moreover, fuzzy-time-interval periodic patterns that are generated encourages to find daily or anomalies resident’s habits.
APA, Harvard, Vancouver, ISO, and other styles
26

Hu, Xiaohua. "Data-Mining and Uncertain Reasoning: An Integrated Approach." Information Visualization 2, no. 1 (March 2003): 78–79. http://dx.doi.org/10.1057/palgrave.ivs.9500041.

Full text
APA, Harvard, Vancouver, ISO, and other styles
27

Li, Jinhong, Lizhen Wang, Hongmei Chen, and Zhengbao Sun. "Mining spatial high-average utility co-location patterns from spatial data sets." Intelligent Data Analysis 26, no. 4 (July 11, 2022): 911–31. http://dx.doi.org/10.3233/ida-215848.

Full text
Abstract:
The spatial co-location pattern refers to a subset of non-empty spatial features whose instances are frequently located together in a spatial neighborhood. Traditional spatial co-location pattern mining is mainly based on the frequency of the pattern, and there is no difference in the importance or value of each spatial feature within the pattern. Although the spatial high utility co-location pattern mining solves this problem, it does not consider the effect of pattern length on the utility. Generally, the utility of the pattern also increases as the length of the pattern increases. Therefore, the evaluation criterion of the high utility co-location mining is unfair to the short patterns. In order to solve this problem, this paper first considers the utility and length of the co-location pattern comprehensively, and proposes a more reasonable High-Average Utility Co-location Pattern (HAUCP). Then, we propose a basic algorithm based on the extended average utility ratio of co-location patterns to mining all HAUCPs, which solves the problem that the average utility ratio of patterns does not satisfy the downward closure property. Next, an improved algorithm based on the local extended average utility ratio is developed which effectively reduces the search space of the basic algorithm and improves the mining efficiency. Finally, the practicability and robustness of the proposed method are verified based on real and synthetic data sets. Experimental results show that the proposed algorithm can effectively and efficiently find the HAUCPs from spatial data sets.
APA, Harvard, Vancouver, ISO, and other styles
28

Lina Zhou, Yongmei Shi, Jinjuan Feng, and A. Sears. "Data mining for detecting errors in dictation speech recognition." IEEE Transactions on Speech and Audio Processing 13, no. 5 (September 2005): 681–88. http://dx.doi.org/10.1109/tsa.2005.851874.

Full text
APA, Harvard, Vancouver, ISO, and other styles
29

Cui, Zongyong, Zongjie Cao, Jianyu Yang, and Hongliang Ren. "Hierarchical Recognition System for Target Recognition from Sparse Representations." Mathematical Problems in Engineering 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/527095.

Full text
Abstract:
A hierarchical recognition system (HRS) based on constrained Deep Belief Network (DBN) is proposed for SAR Automatic Target Recognition (SAR ATR). As a classical Deep Learning method, DBN has shown great performance on data reconstruction, big data mining, and classification. However, few works have been carried out to solve small data problems (like SAR ATR) by Deep Learning method. In HRS, the deep structure and pattern classifier are combined to solve small data classification problems. After building the DBN with multiple Restricted Boltzmann Machines (RBMs), hierarchical features can be obtained, and then they are fed to classifier directly. To obtain more natural sparse feature representation, the Constrained RBM (CRBM) is proposed with solving a generalized optimization problem. Three RBM variants,L1-RNM,L2-RBM, andL1/2-RBM, are presented and introduced to HRS in this paper. The experiments on MSTAR public dataset show that the performance of the proposed HRS with CRBM outperforms current pattern recognition methods in SAR ATR, like PCA + SVM, LDA + SVM, and NMF + SVM.
APA, Harvard, Vancouver, ISO, and other styles
30

Lu, Guo Sheng, Chen Sheng Wang, Hai Lu Yang, and Li Chang Zhao. "Research and Analysis of Data Mining Based on Clustering Algorithm." Applied Mechanics and Materials 602-605 (August 2014): 3321–24. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3321.

Full text
Abstract:
Data mining is the frontier research topic in the field of information processing and database technology, is recognized as one of the most promising key technologies. Data mining is a collection of statistics, machine learning, database, pattern recognition, artificial intelligence and other disciplines, which is a new inter-discipline. Data mining put more emphasis on discovering implicit knowledge in huge amounts of data and the scalability of the algorithm, and is a technology very close to the actual use, with high technical content, bigger implementation difficulty.
APA, Harvard, Vancouver, ISO, and other styles
31

Giraud-Carrier, C., and O. Povel. "Characterising Data Mining software." Intelligent Data Analysis 7, no. 3 (July 23, 2003): 181–92. http://dx.doi.org/10.3233/ida-2003-7302.

Full text
APA, Harvard, Vancouver, ISO, and other styles
32

Takagi, Noboru. "An Application of Binary Decision Trees to Pattern Recognition." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 5 (September 20, 2006): 682–87. http://dx.doi.org/10.20965/jaciii.2006.p0682.

Full text
Abstract:
Decision rules are a key technique in decision making, data mining and knowledge discovery in databases. We introduce an application of decision rules, handwriting pattern classification. When decision rules are applied to pattern recognition, one rule forms a hyperrectangle in feature space, i.e., each decision rule corresponds to one hyperrectangle. This means that a set of decision rules is considered a classification system, called the subclass method. We apply decision rules to handwritten Japanese character recognition, showing experimental results.
APA, Harvard, Vancouver, ISO, and other styles
33

Thomas, Jyothi, and G. Kulanthaivel. "Data Mining Approach in Preterm Birth Prediction." Mapana - Journal of Sciences 9, no. 1 (May 31, 2010): 18–30. http://dx.doi.org/10.12723/mjs.16.3.

Full text
Abstract:
Data mining refers to the process of discovering patterns in data, typically with the aid of powerful algorithms to automate part of the search. These methods come from the disciplines such as statistics, machine learning, pattern recognition, neural networks and database. In particular this paper reveals out how the problem of preterm birth prediction is approached by a data mining analyst with a background in machine learning. In the health field, data mining applications have been growing considerably as it can be used to directly derive patterns, which are relevant to forecast different risk groups among the patients. Data mining technique such as clustering has not been used to predict preterm birth. Hence this paper made an attempt to identify patterns from the database of the preterm birth patients using clustering.
APA, Harvard, Vancouver, ISO, and other styles
34

Shneiderman, Ben. "Inventing Discovery Tools: Combining Information Visualization with Data Mining." Information Visualization 1, no. 1 (March 2002): 5–12. http://dx.doi.org/10.1057/palgrave.ivs.9500006.

Full text
Abstract:
The growing use of information visualization tools and data mining algorithms stems from two separate lines of research. Information visualization researchers believe in the importance of giving users an overview and insight into the data distributions, while data mining researchers believe that statistical algorithms and machine learning can be relied on to find the interesting patterns. This paper discusses two issues that influence design of discovery tools: statistical algorithms vs visual data presentation, and hypothesis testing vs exploratory data analysis. The paper claims that a combined approach could lead to novel discovery tools that preserve user control, enable more effective exploration, and promote responsibility.
APA, Harvard, Vancouver, ISO, and other styles
35

Kamiya, Yohei, and Hirohisa Seki. "Distributed Mining of Closed Patterns from Multi-Relational Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 804–9. http://dx.doi.org/10.20965/jaciii.2015.p0804.

Full text
Abstract:
In multi-relational data mining (MRDM), there have been proposed many methods for searching for patterns that involve multiple tables (relations) from a relational database. In this paper, we consider closed pattern mining from distributed multi-relational databases (MRDBs). Since the computation of MRDM is costly compared with the conventional itemset mining, we propose some efficient methods for computing closed patterns using the techniques studied in Inductive Logic Programming (ILP) and Formal Concept Analysis (FCA). Given a set oflocaldatabases, we first compute sets of their closed patterns (concepts) using a closed pattern mining algorithm tailored to MRDM, and then generate the set of closed patterns in the global database by utilizing themergeoperator. We also present some experimental results, which shows the effectiveness of the proposed methods.
APA, Harvard, Vancouver, ISO, and other styles
36

Tang, Xiaoqiang, Ming Zhang, Haifeng Shi, and Changjie Pan. "Image Pattern Recognition Combined With Data Mining for Diagnosis and Detection of Myocardial Infarction." IEEE Access 8 (2020): 146085–92. http://dx.doi.org/10.1109/access.2020.3014724.

Full text
APA, Harvard, Vancouver, ISO, and other styles
37

Gujral, Harshit, Sangeeta Mittal, and Abhinav Sharma. "A Novel Data Mining Approach for Analysis and Pattern Recognition of Active Fingerprinting Components." Wireless Personal Communications 105, no. 3 (February 11, 2019): 1039–68. http://dx.doi.org/10.1007/s11277-019-06135-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
38

Dotsenko, G. S., and A. S. Dotsenko. "Conserved Peptides Recognition by Ensemble of Neural Networks for Mining Protein Data – LPMO Case Study." Mathematical Biology and Bioinformatics 15, no. 2 (December 22, 2020): 429–40. http://dx.doi.org/10.17537/2020.15.429.

Full text
Abstract:
Mining protein data is a recent promising area of modern bioinformatics. In this work, we suggested a novel approach for mining protein data – conserved peptides recognition by ensemble of neural networks (CPRENN). This approach was applied for mining lytic polysaccharide monooxygenases (LPMOs) in 19 ascomycete, 18 basidiomycete, and 18 bacterial proteomes. LPMOs are recently discovered enzymes and their mining is of high relevance for biotechnology of lignocellulosic materials. CPRENN was compared with two conventional bioinformatic methods for mining protein data – profile hidden Markov models (HMMs) search (HMMER program) and peptide pattern recognition (PPR program combined with Hotpep application). The maximum number of hypothetical LPMO amino acid sequences was discovered by HMMER. Profile HMMs search proved to be more sensitive method for mining LPMOs than conserved peptides recognition. Totally, CPRENN found 76 %, 67 %, and 65 % of hypothetical ascomycete, basidiomycete, and bacterial LPMOs discovered by HMMER, respectively. For AA9, AA10, and AA11 families which contain the major part of all LPMOs in the carbohydrate-active enzymes database (CAZy), CPRENN and PPR + Hotpep found 69–98 % and 62–95 % of amino acid sequences discovered by HMMER, respectively. In contrast with PPR + Hotpep, CPRENN possessed perfect precision and provided more complete mining of basidiomycete and bacterial LPMOs.
APA, Harvard, Vancouver, ISO, and other styles
39

Qu, Nianhua, Yubiao Yan, Tong Cheng, Yajun Wang, Xin Song, and Limin Wang. "Mining Engineering Image Recognition Method Based on Simulated Annealing Algorithm." Mobile Information Systems 2022 (May 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/1832836.

Full text
Abstract:
Because the current methods used in mining engineering image feature recognition have some problems, such as poor classification accuracy, operation efficiency, and inability to recognize rotation features, in order to promote the development of mineral processing in China and improve resource recovery, simulated annealing algorithm is applied to the process of mining engineering image feature extraction in this paper. Based on the simulated annealing algorithm, this paper introduces the image recognition technology based on the simulated annealing algorithm and uses this image recognition technology to study the separation of ore and rock according to the differences between ore and waste rock in morphology and R, G, and B primary color components. At the same time, based on the local binary mode theory, the local variance of pixels is calculated successively to obtain the variance diagram of mining engineering image. At the same time, the simulated annealing algorithm is used to calculate the vector in each direction in the variance diagram of mining engineering image, and then, the vector is combined as the image eigenvalue. The obtained eigenvalue is combined with the binary pattern feature to realize mining recognition method and feature recognition. Finally, the experimental research shows that the algorithm proposed in this paper can quickly extract the spatial data information of mining engineering image variance and reuse the information of image local binary pattern. Compared with the traditional mining engineering image feature extraction algorithm, the recognition accuracy of this algorithm can reach 85%.
APA, Harvard, Vancouver, ISO, and other styles
40

Sairam, B. V. V. S. "Human Activity Pattern Prediction System for Smart Home Appliances." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1811–14. http://dx.doi.org/10.22214/ijraset.2021.39628.

Full text
Abstract:
Abstract: This paper proposes a model (HAPP) for learning and finding human action designs for Smart home applications based on huge amounts of data from smart homes. The proposed methodology quantifies and breaks down vitality use variations initiated by renters' behaviour using visit design mining, group research, and expectation. The HAPP System addresses the legal obligation to deconstruct energy consumption patterns at the machine level, which is directly linked to the actions of human. In the quantum/information cut of 24th, the information from shrewd meter is recursively mined, and the results are stored up throughout progressive mining works out. The HAPP System specifies the conditions for analysing the project that we use Keywords: Smart home, Data Mining, classifications, Human activity recognition
APA, Harvard, Vancouver, ISO, and other styles
41

Huang, Jin Xin, Ya Jin Li, Yang Jiang, Jie Zhan, Lin Niu, Jin Tao Cui, Meng Chao Ma, Deng Sen He, and Guo Tao Lu. "The Online Pattern Recognition Method of SF6 Gas Leakage Based on Image Recognition." Applied Mechanics and Materials 738-739 (March 2015): 682–85. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.682.

Full text
Abstract:
Based on image processing of SF6 gas leakage on-line pattern recognition method, this paper achieves gas leakage feature extracting, on-line identification of gas leakage and leakage points, SF6 gas leakage can be on-line automatic identification. The simulation results show the feasibility of the algorithm. Compared with the traditional method, paper provides a more intuitive discrimination basis for field staff , as well as for the depth of the late testing data mining provides a research way of thinking
APA, Harvard, Vancouver, ISO, and other styles
42

Halkidi, M., D. Spinellis, G. Tsatsaronis, and M. Vazirgiannis. "Data mining in software engineering." Intelligent Data Analysis 15, no. 3 (May 4, 2011): 413–41. http://dx.doi.org/10.3233/ida-2010-0475.

Full text
APA, Harvard, Vancouver, ISO, and other styles
43

LAU, RAYMOND Y. K., YUEFENG LI, SHENG-TANG WU, and XUJUAN ZHOU. "SEQUENTIAL PATTERN MINING AND NONMONOTONIC REASONING FOR INTELLIGENT INFORMATION AGENTS." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 04 (June 2007): 773–89. http://dx.doi.org/10.1142/s0218001407005673.

Full text
Abstract:
With the explosive growth of information available on the Internet, more effective data mining and data reasoning mechanism is required to process the sheer volume of information. Belief revision logic offers the expressive power to represent information retrieval contexts, and it also provides a sound inference mechanism to model the nonmonotonicity arising in changing retrieval contexts. Contextual knowledge for information retrieval can be extracted via efficient sequential pattern mining. We present a pattern taxonomy extraction model which efficiently performs the task of discovering descriptive frequent sequential patterns by pruning the noisy associations. This paper illustrates a novel approach of integrating the sequential data mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power. Initial experiments show that our belief revision logic and sequential pattern mining based intelligent information agents outperform the vector space model based information agents. Our work opens the door to the development of next generation of intelligent information agents to alleviate the information overload problem.
APA, Harvard, Vancouver, ISO, and other styles
44

Du, Shengli, Mingchao Li, Shuai Han, Jonathan Shi, and Heng Li. "Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data." Energies 12, no. 6 (March 14, 2019): 992. http://dx.doi.org/10.3390/en12060992.

Full text
Abstract:
The electric power industry is an essential part of the energy industry as it strengthens the monitoring and control management of household electricity for the construction of an economic power system. In this paper, a non-intrusive affinity propagation (AP) clustering algorithm is improved according to the factor graph model and the belief propagation theory. The energy data of non-intrusive monitoring consists of the actual energy consumption data of each electronic appliance. The experimental results show that this improved algorithm identifies the basic and combined class of home appliances. According to the possibility of conversion between different classes, the combination of classes is broken down into different basic classes. This method provides the basis for power management companies to allocate electricity scientifically and rationally.
APA, Harvard, Vancouver, ISO, and other styles
45

Ehrenman, Gayle. "Mining What Others Miss." Mechanical Engineering 127, no. 02 (February 1, 2005): 26–31. http://dx.doi.org/10.1115/1.2005-feb-1.

Full text
Abstract:
This article discusses data mining that draws upon extensive work in areas such as statistics, machine learning, pattern recognition, databases, and high-performance computing to discover interesting and previously unknown information in data. More specifically, data mining is the analysis of 10 large data sets to find relationships and patterns that aren’t readily apparent, and to summarize the data in new and useful ways. Data mining technology has enabled earth scientists from NASA to discover changes in the global carbon cycle and climate system, and biologists to map and explore the human genome. Data mining is not restricted solely to vast banks of data with unlimited ways of analyzing it. Manufacturers, such as W.L. Gore (the maker of GoreTex) use commercially available data mining tools to warehouse and analyze their data, and improve their manufacturing process. Gore uses data mining tools from analytic software vendor SAS for statistical modeling in its manufacturing process.
APA, Harvard, Vancouver, ISO, and other styles
46

Petry, Fred, and Ronald Yager. "Intuitionistic and Interval-Valued Fuzzy Set Representations for Data Mining." Algorithms 15, no. 7 (July 19, 2022): 249. http://dx.doi.org/10.3390/a15070249.

Full text
Abstract:
Data mining refers to a variety of techniques in the fields of databases, machine learning and pattern recognition. The intent is to obtain useful patterns and associations from a large collection of data. In this paper we describe extensions to the attribute generalization process to deal with interval and intuitionistic fuzzy information. Specifically, we consider extensions for using interval-valued fuzzy representations in both data and the generalization hierarchy. Moreover, preliminary representations using intuitionistic fuzzy information for attribute generalization are described. Finally, we consider how to use fuzzy hierarchies for the generalization of interval-valued fuzzy representations.
APA, Harvard, Vancouver, ISO, and other styles
47

Sumarauw, Sylvia. "Fuzzy c-Means Clustering untuk Pengenalan Pola Studi kasus Data Saham." Jurnal Axioma : Jurnal Matematika dan Pembelajaran 7, no. 2 (November 4, 2022): 97–106. http://dx.doi.org/10.56013/axi.v7i2.1395.

Full text
Abstract:
Fuzzy Clustering is one of the five roles used by data mining experts to transform large amounts of data into useful information, and one method that is often and widely used is Fuzzy c-Means (FCM) Clustering. FCM is a data clustering technique where the existence of each data point in the cluster is based on the degree of membership. This study aims to see the pattern of data samples or data categories using FCM clustering. The analyzed data is stock data on Jakarta Stock Exchange (BEJ) in the Property and Real Estate sector (issuer group). The data mining processes comply Cross Industry Standard Process Model for Data mining Process (Crisp-DM), with several stages, starting with the stage of getting to know the business process (Business Understanding) then studying the data (Data Understanding), continuing with the Data Preparation stage, Modeling stage, Evaluation stage and finally the Deployment stage. In the modeling stage, the FCM model is used. FCM clustering model data mining can analyze data in large databases with many variables and complicated, especially to get patterns from the data. Then a Fuzzy Inference System (FIS) was built based on a known pattern for simulating input data into output data based on fuzzy logic. Keywords: Fuzzy c-Means Clustering, Pattern Recognition
APA, Harvard, Vancouver, ISO, and other styles
48

MANIMALA, K., K. SELVI, and R. AHILA. "POWER DISTURBANCES PATTERN RECOGNITION USING SUPPORT VECTOR MACHINE." International Journal of Information Acquisition 08, no. 01 (March 2011): 53–64. http://dx.doi.org/10.1142/s0219878911002331.

Full text
Abstract:
Recently, many signal processing techniques, such as fast Fourier transform, short-time Fourier transform, wavelet transform (WT), and wavelet packet transform (WPT), have been applied to detect, identify, and classify power quality (PQ) disturbances. For research on PQ analysis, it is critical to apply the appropriate signal processing techniques and classifier to solve PQ problems. The aim of this paper is to develop a classification method based on the combination of Hilbert transform (HT) and support vector machine (SVM) for the assessment of power quality events. Recent data mining literature has shown that support vector machine methods generally outperform traditional statistical and neural methods in classification problems involving power disturbance signals. The features obtained from the Hilbert transform are distinct, understandable and immune to noise. Analysis is presented to verify that the merits of HT and SVM combination make it adequate for PQ analysis when compared with the existing techniques in the literature.
APA, Harvard, Vancouver, ISO, and other styles
49

Yang, Hang, Simon Fong, Kyungeun Cho, and Junbo Wang. "Atmospheric pattern recognition of human activities on ubiquitous sensor network using data stream mining algorithms." International Journal of Sensor Networks 20, no. 3 (2016): 147. http://dx.doi.org/10.1504/ijsnet.2016.075364.

Full text
APA, Harvard, Vancouver, ISO, and other styles
50

Iqbal, Mehwish, M. Mohsin Riaz, Abdul Ghafoor, Syed Sohaib Ali, and Attiq Ahmad. "Guided image filtering using data mining and decomposition." Imaging Science Journal 67, no. 5 (July 3, 2019): 261–67. http://dx.doi.org/10.1080/13682199.2019.1631049.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the bibliographies on the topic '080109 Pattern Recognition and Data Mining' for other source types:
Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography