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Probabilistic REpresentatives mining (PREM): A Clustering Method for Distributional data Reduction

AIAA JOURNAL 2022年第4期60卷 2580-2596页

作者： Gao, Zhenyu Puranik, Tejas G. Mavris, Dimitri N. Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

Complex computations and analyses on massive data sets can be impractical or infeasible. data reduction is a crucial problem in the era of big data to obtain a reduced representation of the data set to facilitate more efficient yet accurate analyses. To best preserve the integrity of the original data set, a reduced representation aims to best maintain the same data distribution, referred to as a probabilistically representative subset. This paper considers the problem of reducing a large data set to very small such subsets at which random sampling does not perform well enough. We propose a data mining approach called Probabilistic Representatives mining (PREM) to tackle this challenge. PREM uses balanced clustering to prevent undersampling and oversampling issues and multistage computing strategy to achieve better scalability and consistency. Numerical experiments on typical probability distributions and real-world data sets in the field of aeronautics and astronautics demonstrate PREM's superiority over the baselines. An uncertainty quantification case study from aviation environmental impact modeling further shows PREM's effectiveness and accuracy in generating probabilistically representative small samples for costly computations. Potential limitations and extensions of the method are also discussed in the paper.

关键词： data Reduction Computing Strategies Continuous Probability Distributions data mining algorithms Air Transportation System Astronautics Aeronautics Aviation Environmental Design Tool Radial Basis Function Kernel LANDSAT

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Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study

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GEODERMA 2019年 341卷 111-128页

作者： Ji, Wenjun Adamchuk, Viacheslav I. Chen, Songchao Su, Ahmad S. Mat Ismail, Ashraf Gan, Qianjun Shi, Zhou Biswas, Asim McGill Univ Dept Bioresource Engn Montreal PQ H9X 3V9 Canada Swedish Univ Agr Sci SLU Dept Soil & Environm Precis Agr & Pedometr SE-53223 Skara Sweden INRA Unite Infosol F-45075 Orleans France Univ Putra Malaysia Dept Agr Technol Serdang 43400 Selangor Malaysia McGill Univ Dept Food Sci & Agr Chem Montreal PQ H9X 3V9 Canada Zhejiang Univ Inst Appl Remote Sensing & Informat Technol Hangzhou 310058 Zhejiang Peoples R China Univ Guelph Sch Environm Sci Guelph ON N1G 2W1 Canada

In this research, proximal soil sensor data fusion was defined as a multifaceted process which integrates geospatially correlated data, or information, from multiple proximal soil sensors to accurately characterize the spatial complexity of soils. This has capability of providing improved understanding of soil heterogeneity for potential applications associated with crop production and natural resource management. To assess the potential of data fusion for the purpose of improving thematic soil mapping over the single sensor approach, data from multiple proximal soil sensors were combined to develop and validate predictive relationships with laboratory-measured soil physical and chemical properties. The work was conducted in an agricultural field with both mineral and organic soils. The integrated data included: topography records obtained using a real-time kinetic (RTK) global navigation satellite system (GNSS) receiver, apparent soil electrical conductivity (ECa) obtained using an electromagnetic induction sensor, and content of several naturally occurring radioisotopes detected using a mobile gamma-ray spectrometer. In addition, the soil profile data were collected using a commercial ruggedized multi-sensor platform carrying a visible and near-infrared (vis-NIR) optical sensor and a galvanic contact soil ECa sensor. The measurements were carried out at predefined field locations covering the entire study area identified from sensor measured a priori information on field elevation, ECa and gamma-ray count. The information was used to predict: soil organic matter (SOM), pH, lime buffer capacity (LBC), as well as concentration of phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and aluminum (Al). Partial least squares regressions (PLSRs) were used to predict soil properties from individual sensors and different sensor combinations (sensor data fusion). By integrating the data from all of the proximal soil sensors, SOM, pH, LBC, Ca, Mg, and Al were predic

关键词： Vis-NIR spectroscopy Gamma-ray Apparent electrical conductivity Sensor fusion data fusion Partial least square regression data mining algorithms

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Improved Instrument Approach Stability Analysis for Helicopters Using data Fusion and Analytics

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2022年第11期19卷 729-746页

作者： Robinson, Joseph N. Payan, Alexia P. Johnson, Charles Mavris, Dimitri N. Georgia Inst Technol Sch Aerosp Engn Aerosp Syst Design Lab 270 Ferst Dr Atlanta 303320150 Georgia FAA Sch Aerosp Engn Aerosp Syst Design Lab 270 Ferst Dr Atlantic City NJ 08405 USA Georgia Inst Technol Sch Aerosp Engn 270 Ferst Dr Atlanta 303320150 Georgia

Approach and landing are among the most challenging and dangerous tasks required of helicopter pilots. Helicopter accident statistics suggest that the majority of helicopter accidents occur during the approach and landing phases of flight. Unstabilized approaches are a leading cause of these accidents due to the increased potential for loss of control, loss of situational awareness, or controlled flight into terrain. In the present work, routine flight data records obtained as part of a voluntary helicopter flight data monitoring program are used to identify, analyze, and evaluate the stability of instrument approach events. A data fusion process is developed that enables detection of the approach phase, identification of an associated instrument approach procedure, analysis of approach stability, and knowledge discovery through data mining. The process identified 385 instrument approach events in a set of 7063 operational helicopter flight data records. Manual analysis of these events reveals some common errors, including crossing the final approach fix far below the assigned altitude and violating the minimum altitude requirement during initial and intermediate approach legs and course reversal maneuvers. data mining algorithms are tested and found to be capable of automatically identifying atypical or unstable approach events.

关键词： Rotor Systems Instrument Approach Procedure data Fusion Helicopter Flight data Monitoring data mining algorithms Controlled Flight into Terrain Instrument Flight Rules Helipad Philadelphia International Airport Rotorcrafts

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An Improved Algorithm and Implementation of data mining for Intelligent Manufacturing Association Rules Based on Pattern Recognition

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IEEE CONSUMER ELECTRONICS MAGAZINE 2023年第2期12卷 94-99页

作者： Guo, Yuan Zhou, Jingyong Qin, Qiang Wei, Yun Zhang, Weitang Hefei Univ Sch Adv Mfg Engn Hefei Anhui Peoples R China Hefei Univ Hefei Technol Coll Sch Adv Mfg Engn Hefei Anhui Peoples R China

With the deepening of the research of artificial intelligence theory, the research of pattern recognition has made further development, and the applicable fields have also been continuously expanded. Pattern recognition can be used in text and speech recognition, remote sensing nuclear medicine diagnosis. This article optimizes and analyzes intelligent manufacturing based on pattern recognition, and uses improved association rule data mining algorithms to improve it from many aspects, Association rules are not limited by the number of dependent variables and can discover associations between data in large databases. data analysis is carried out based on factors in different production stages of intelligent manufacturing to demonstrate the feasibility of the improved algorithm of association rule data mining. The experimental simulation results show that the application of the improved pattern recognition model in this research has made the estimated value of the remanufactured product remanufactured to a maximum of 4.05, and the production effect has also been greatly improved, the floating range is between 6.13 and 7.90, and the overall evaluation can reach 9.06. The experimental results show that the effect of the improved pattern recognition model is greatly improved.

关键词： Pattern recognition data mining Manufacturing Production Feature extraction data models Training Pattern Recognition Intelligent Manufacturing data Association Rules data mining algorithms

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Proposed S-Algo plus data mining algorithm for web platforms course content and usage evaluation

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SOFT COMPUTING 2020年第19期24卷 14861-14883页

作者： Kazanidis, Ioannis Valsamidis, Stavros Gounopoulos, Elias Kontogiannis, Sotirios Int Hellen Univ Adv Educ Technol & Mobile Applicat Lab Kavala Greece Int Hellen Univ Dept Accounting & Finance Kavala Greece TEI Western Macedonia Dept Business Adm Grevena Greece Univ Ioannina Dept Math Ioannina Greece

This paper suggests a novel data mining algorithm for the evaluation of e-learning courses from a Learning Management System. This new algorithm, which is called S-Algo+ (Superposition Algorithm), takes as input the course rankings and the suggestion results from any kind of ranking/hierarchical algorithms and evaluates the validity of a course ranking position. The ranking algorithms estimate the quantity and quality of the course content according to users' actions and interest. S-Algo+ generates an improved final ranking suggestion output, combining the best results of the source ranking algorithms using statistical and mathematic techniques. In this way, the researchers and course instructors can use more accurate results. The efficiency and applicability of the S-Algo+ algorithm was evaluated successfully with a cross-comparison quantitative and qualitative process in a case study at a Greek university. Our new proposed S-Algo+ algorithm may lead to both theoretical and practical advantages. It may also apply not only for course evaluation but for any kind of web application such as e-commerce.

关键词： data mining algorithms Web mining in education e-Learning course ranking Course knowledge discovery and management Learning analytics

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Toward an improved learning process: the relevance of ethnicity to data mining prediction of students' performance

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SN APPLIED SCIENCES 2020年第1期2卷 8-8页

作者： Adekitan, Aderibigbe Israel Salau, Odunayo Tech Univ Ilmenau Dept Elect Engn & Informat Technol Ilmenau Germany Covenant Univ Dept Business Management Ota Ogun State Nigeria

The ability to predict failure is an advantageous educational tool that can be effectively used to counsel student, and this may also be used as a tool for developing, and channelling adequate academic interventions toward preventing failure and dropout tendencies. Students are generally admitted based on their evaluated academic potentials as measured using their admission criteria scores. This study seeks to identify the relationship, if any, between the admission criteria scores and the graduation grades, and to examine the influence of ethnicity using the geopolitical zone of origin of the student on the predictive accuracy of the models developed using a Nigerian University as a case study. data mining analyses were carried out using four classifiers on the Orange Software, and the results were verified with multiple regression analysis. The maximum classification accuracy observed is 53.2% which indicates that the pre-admission scores alone are insufficient for predicting the graduation result of students but it may serve as a useful guide. By applying over-sampling technique, the accuracy increased to 79.8%. The results establish that the ethnic background of the student is statistically insignificant in predicting their graduation results. Hence, the use of ethnicity in admission processes is therefore not ideal.

关键词： Educational data mining Nigerian university data mining algorithms Performance evaluation methodologies Knowledge discovery Machine learning

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Geometric data perturbation for privacy preserving outsourced data mining

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KNOWLEDGE AND INFORMATION SYSTEMS 2011年第3期29卷 657-695页

作者： Chen, Keke Liu, Ling Wright State Univ Dept Comp Sci & Engn Dayton OH 45435 USA Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

data perturbation is a popular technique in privacy-preserving data mining. A major challenge in data perturbation is to balance privacy protection and data utility, which are normally considered as a pair of conflicting factors. We argue that selectively preserving the task/model specific information in perturbation will help achieve better privacy guarantee and better data utility. One type of such information is the multidimensional geometric information, which is implicitly utilized by many data-mining models. To preserve this information in data perturbation, we propose the Geometric data Perturbation (GDP) method. In this paper, we describe several aspects of the GDP method. First, we show that several types of well-known data-mining models will deliver a comparable level of model quality over the geometrically perturbed data set as over the original data set. Second, we discuss the intuition behind the GDP method and compare it with other multidimensional perturbation methods such as random projection perturbation. Third, we propose a multi-column privacy evaluation framework for evaluating the effectiveness of geometric data perturbation with respect to different level of attacks. Finally, we use this evaluation framework to study a few attacks to geometrically perturbed data sets. Our experimental study also shows that geometric data perturbation can not only provide satisfactory privacy guarantee but also preserve modeling accuracy well.

关键词： Privacy-preserving data mining data perturbation Geometric data perturbation Privacy evaluation data mining algorithms

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Optimization model of battery electric vehicle charging facility layout towards embedded system and data mining algorithm

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INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS 2024年第5期25卷 573-582页

作者： Zhang, Pengcheng Liu, Jinsen Luo, Ning Chen, Ludong Wang, Jie Guizhou Power Grid Co Ltd Power Grid Planning & Res Ctr Guiyang 550003 Peoples R China

Battery electric vehicles have been developing rapidly in recent years due to their advantages of energy saving and emission reduction. However, the development of battery electric vehicles is still in its infancy due to the relatively immature charging technology and the lack of infrastructure development. In many researches, there have been many solutions to adopt remote monitoring systems in battery electric vehicles to monitor and collect them in real time for statistical analysis and fault diagnosis. However, the current monitoring systems are carried out statistically and it is difficult to extract much valuable information from the data. In this paper, after analyzing the construction mode of battery electric vehicle charging facilities, the battery electric vehicle charging facility system platform using embedded system was analyzed. Several functions were simulated to meet the problems encountered by users in using it, studying the battery electric vehicle charging facility layout optimization model based on data mining algorithm. Focusing on the coverage problem in the siting problem, the State-of-Charge (SOC) of the battery charge state was analyzed. Taking residential areas as an example and 4 weeks as a cycle, it was found that during the first to fourth weeks, the negative power at 1:00, 20:00 and 24:00 was higher. At the same type of time point, different charging thresholds had a significant impact on the charging load demand of users. The larger the charging threshold of battery electric vehicles appeared, the earlier the peak moment and the higher the peak load. The battery state of the battery electric vehicle in the work area is 60 % at 1:00, and it reaches a relatively full state of 90 % at 8:00. In different regions, the allocation of charging thresholds for battery electric vehicles was different, and the Markov theory was derived from the analysis of the coverage problem to enable fast and effective siting of charging piles while satisfying c

关键词： battery electric vehicles charging facility layout embedded systems data mining algorithms coverage issues

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A top-down method for mining most specific frequent patterns in biological sequence data

A top-down method for mining most specific frequent patterns...

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4th SIAM International Conference on data mining

作者： Ester, M Zhang, X Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)0898715687

The emergence of automated high-throughput sequencing technologies has resulted in a huge increase of the amount of DNA and protein sequences available in public databases. A promising approach for mining such biological sequence data is mining frequent subsequences. One way to limit the number of patterns discovered is to determine only the most specific frequent subsequences which subsume a large number of more general patterns. In the biological domain, a wealth of knowledge on the relationships between the symbols of the underlying alphabets (in particular, amino acids) of the sequences has been acquired, which can be represented in concept graphs. Using such concept graphs, much longer frequent patterns can be discovered which are more meaningful from a biological point of view. In this paper, we introduce the problem of mining most specific frequent patterns in biological data in the presence of concept graphs. While the well-known methods for frequent sequence mining typically follow the paradigm of bottom-up pattern generation, we present a novel top-down method (ToMMS) for mining such patterns. ToMMS (1) always generates more specific patterns before more general ones and (2) performs only minimal generalizations of infrequent candidate sequences. Due to these properties, the number of patterns generated and tested is minimized. Our experimental results demonstrate that ToMMS clearly outperforms state-of-the-art methods from the bioinformatics community as well as from the data mining community for reasonably low minimum support thresholds.

关键词： data mining algorithms sequence data efficiency data mining applications in bioinformatics

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Addressing data Sparsity in Collaborative Filtering Based Recommender Systems Using Clustering and Artificial Neural Network 11

Addressing Data Sparsity in Collaborative Filtering Based Re...

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IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Althbiti, Ashrf Alshamrani, Rayan Alghamdi, Tami Lee, Stephen Ma, Xiaogang Univ Idaho Dept Comp Sci Moscow ID 83844 USA Taif Univ Dept Informat Technol At Taif 26571 Makkah Saudi Arabia Albaha Univ Dept Comp Sci Albaha 65527 Saudi Arabia Univ Idaho Dept Math & Stat Sci Moscow ID 83844 USA

ISBN: (纸本)9781665414906

Collaborative Filtering (CF) is fundamentally characterized by Recommender Systems (RSs), which have recently attracted researchers' attention. The ever-increasing data about users and items and the emergence of machine learning approaches have motivated the recent development of CF. The sparsity caused by the lack of recorded transactions and data makes it challenging for CF to distinguish between users' similar preferences. As a result of the data sparsity issue, CF ultimately lacks the ability to generate useful recommendations and suffers from poor performance. This paper proposes a novel model that uses clustering and artificial neural network to address the issue of data sparsity in CF. The proposed model CANNBCF, a short name for Clustering and Artificial Neural Network Based Collaborative Filtering, is evaluated using four different datasets from four popular domains (books, music, jokes, and movies). The proposed model shows its superiority to solve the sparsity issue that the traditional CF technique encounters. In this paper, eight experiments are conducted to evaluate the performance of CANNBCF. The evaluation criteria include accuracy, precision, recall, F1-score, and Receiver Operating Characteristics used to examine the proposed model. The results of the experiments show that CANNBCF effectively solves the sparsity issue, improves the quality of recommendations, and demonstrates promising prediction accuracy.

关键词： Collaborative Filtering data mining algorithms data Sparsity Evaluation Recommender Systems

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