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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining, MLDM 2003"

共 1150 条记录，以下是191-200 订阅

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Driver Risk Assessment Using Traffic Violation and Accident data by machine learning Approaches 3

Driver Risk Assessment Using Traffic Violation and Accident ...

引用

3rd IEEE international conference on Intelligent Transportation Engineering (ICITE)

作者： Fang, Aifen Qiu, Chenlu Zhao, Lei Jin, Yongjun Minist Publ Secur Traff Management Res Inst Wuxi Peoples R China

ISBN: (纸本)9781538678312

Along with high speed urbanization and motorization, road traffic accidents have become a severe problem in China. Drivers' operation error and risk-taking behavior is a leading cause of traffic accidents. Under this condition, the demand of drivers' traffic safety assessment keeps increasing, especially for professional drivers like passenger drivers and freight drivers. This work proposes a data mining framework of drivers' traffic safety assessment using drivers' personal information, traffic violations and traffic accident data records. Model validation and result interpretation are given, showing the rationality and usefulness of our proposed approach.

关键词： road traffic accident analysis feature selection machine learning weight of evidence information value classification logistic regression

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data mining based Root-cause Analysis of Performance Bottleneck for Big data Workload 19

Data Mining based Root-cause Analysis of Performance Bottlen...

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19th IEEE international conference on High Performance Computing and Communications (HPCC) / 15th IEEE international conference on Smart City (SmartCity) / 3rd IEEE international conference on data Science and Systems (DSS)

作者： Qi, Weichen Li, Yunchun Zhou, Hongang Li, Wei Yang, Hailong Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China

ISBN: (纸本)9781538625880

Straggler task is commonly considered as the major bottleneck in parallel data processing. Previous work mainly focuses on the coarse-grained straggler detection and optimization such as speculative scheduling. However, fine-grained root-cause analysis of straggler tasks is rarely considered. In addition, existing work simply depends on empirical analysis, which lacks of useful guidance to performance optimization. In this paper, we propose a new methodology of fine-grained straggler root-cause analysis using machine learning. We collect raw metrics from Spark event log and hardware sampling tool, and refine them into high-level metrics for model learning. Then we present the root-cause analysis of stragglers through CART tree. A customized prune method is also applied to improve analysis accuracy. From the analysis, we derive several new findings beyond the well known causes of stragglers. Our work provides a new perspective on identifying and understanding the inefficiency in parallel data processing programs by applying machine learning techniques to fine-grained root-cause analysis of straggler tasks.

关键词： Task analysis Measurement Sparks Hardware Resource management Optimization Big data

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Recent trends in data mining (DM): Document clustering of DM publications

Recent trends in data mining (DM): Document clustering of DM...

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3rd international conference on Service Systems and Service Management

作者： Peng, Yi Kou, Gang Chen, Zhengxin Shi, Yong Univ Nebraska Peter Kiewit Inst Informat Sci Technol & Engn Omaha NE 68182 USA Grad Univ Chinese Acad Sci Chinese Acad Sci Res Ctr Data Technol & Knowledge Beijing 100080 Peoples R China

ISBN: (纸本)1424404509

data mining (DM) brings knowledge and theories from several fields including databases, machine learning, optimization, statistics, and data visualization and has been applied to various real-life applications. A large amount of data mining articles have been published. The goal of this study is to establish an overview of the past and current data mining research activities from the title and abstract more than 1400 textual documents collected from premier data mining journals and conference proceedings. Specifically, this study applied document clustering approaches to determine which subjects had been studied over the last several years, which subjects are currently popular, and describe the longitudinal changes of data mining publications.

关键词： document clustering data mining field content analysis categorization

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Improving high-resolution satellite images retrieval using Linear SVM classifier and data augmentation 3

Improving high-resolution satellite images retrieval using L...

引用

3rd international conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Sebai, Houria Kourgli, Assia USTHB Fac Elect & Informat LTIR BP 32 El Alia 16111 Bab Ezzouar Algeria

ISBN: (纸本)9781538642382

The availability of huge remote sensing image dataset imposes recourse to powerful techniques of content-based image retrieval for archiving and mining. This paper propose descriptors based on the SIFT (Scale invariant features) combined with SVM linear classification. To build a powerful image classifier using very little training data, image augmentation is usually required to boost the performance of the classification. For this reason, an augmentation data is used to increase the training data for the SVM (Support vector machine) classifier. The creation of the training data is done using several techniques of augmentation: anisotropic filter. We report a first evaluation of the CBIR (Content based image retrieval) and the second evaluation of the system aims to compare the deep learning with the boosted SVM classification.

关键词： CBIR SIFT SVM classifier descriptors data augmentation

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Design and Implementation of Marketing Classification System Based on Artificial Intelligence 3

Design and Implementation of Marketing Classification System...

引用

3rd IEEE international conference on Electrical Engineering, Big data and Algorithms, EEBDA 2024

作者： Tian, Shuo Shanghai University Shanghai China

ISBN: (纸本)9798350380989

This paper mainly introduces some machine learning methods used in the field of data mining. The method of data mining is discussed by taking market segmentation algorithm as an example. This paper presents an improved support vector machine method. By judging the similarity between each sampling point and its closest class, the classifier is obtained by training it with support vector machine. A method based on stepwise approximation method is proposed to solve the problem of too large size of KNN training samples. The experimental results show that non-SVM is superior to SVM in classification accuracy and classification speed. In addition, this project will also adopt artificial intelligence technology to build a data analysis system for high-end wine, analyze, compare and match massive information data of high-end wine through data mining, and discover the comparison information and market potential of main ingredients in high-end wine. It is compared with the chemical composition of high-grade red wine, and the relevant marketing treatment of high-grade red wine is obtained. It can help high-grade red wine manufacturers to analyze the composition content and quality of high-grade red wine. © 2024 IEEE.

关键词： artificial intelligence data analysis data mining high-end red wine machine learning marketing classification algorithm

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Intrusion Detection using data mining: A contemporary comparative study 3

Intrusion Detection using Data Mining: A contemporary compar...

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3rd international conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Bouteraa, Imad Derdour, Makhlouf Ahmim, Ahmed Univ Larbi Tebessi Dept Comp Sci Tebessa Algeria Univ Larbi Tebessi LAMIS Lab Tebessa Algeria Badji Mokhtar Univ Annaba LRS Lab Annaba Algeria

ISBN: (纸本)9781538642382

Intrusion detection systems play a crucial rule in this era where networks reached almost any sector. Unfortunately, intrusion detection systems are far from perfectness. Therefore, researchers never stopped digging deeper to improve them. In this context, data mining techniques have been highly exploited for intrusion detection. In this paper, we present a comparative study of data mining techniques for intrusion detection. Specifically, we study the overall performances of those methods as well as the impact of training data size on their results. We use ISCX2012 as a benchmark for our experimentation. A realistic dataset that represents at a certain level today's network traffic. The study shows that relatively old methods outperform some of the techniques highly used actually by the community. Regarding the impact of training dataset size, the investigated methods react differently from each other when we add more data to the training dataset. In addition, the results highlight the importance of attack traffic in the training dataset. Moreover, they strongly suggest the use of Random Forest for intrusion detection due to its linear performance relation with the training dataset's size.

关键词： Intrusion detection data mining machine learning ISCX2012

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Application of data mining in physical statistics and evaluation

Application of data mining in physical statistics and evalua...

引用

3rd international conference on international Information Hiding and Multimedia Signal Processing, IIHMSP 2007

作者： Jihui, Zhang Qi, Luo College of P.E. Huazhong Normal University Wuhan 430079 Hubei China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan 430074 China

ISBN: (纸本)0769529941

data mining, which is known as knowledge discovery in databases has been defined as the nontrivial extraction of implicit, previous unknown and potentially useful information from data. It uses machine learning, statistical and visualization techniques to discover and present knowledge in a form which is easily comprehensible to human. In the paper the authors first introduce the idea, basic concept and process of data mining, then, an example and methods of the application of data mining in physical statistics are analyzed. data mining is applied in physical training and evaluation, such as constitution data analyzing, PE industry and competitive sports. Thus;we think data mining becomes an important task of the scientific research of sports topic in future.

关键词： data mining

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A Novel Rule-based Intrusion Detection System Using data mining

A Novel Rule-based Intrusion Detection System Using Data Min...

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3rd IEEE international conference on Computer Science and Information Technology (ICCSIT)

作者： Li, Lei Yang, De-Zhang Shen, Fang-Cheng Nanjing Univ Posts & Telecommun Sch Automat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781424455379

Network security is becoming an increasingly important issue, since the rapid development of the Internet. Network Intrusion Detection System (IDS), as the main security defending technique, is widely used against such malicious attacks. data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. Association rules and sequence rules are the main technique of data mining for intrusion detection. Considering the classical Apriori algorithm with bottleneck of frequent itemsets mining, we propose a Length-Decreasing Support to detect intrusion based on data mining, which is an improved Apriori algorithm. Experiment results indicate that the proposed method is efficient.

关键词： Intrusion Detection Rule-based Length-Decreasing Support Association Rules data mining

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A comparative study of fuzzy classifiers on heart data

A comparative study of fuzzy classifiers on heart data

引用

2011 3rd international conference on Trendz in Information Sciences and Computing, TISC 2011

作者： Anushya, A. Pethalakshmi, A. Department of Computer Science Manonmaniam Sundaranar University Tirunelveli 627 012 India Dindigul-624 001 India

ISBN: (纸本)9781467301329

Fuzzy approaches can play an important role in data mining, because they provide comprehensible results. In addition, the approaches studied in data mining have mainly been oriented at highly structured and precise data. In this paper, we examine the performance of four fuzzy classifiers on heart data. The fusion of Fuzzy Logic with the classifiers Decision Trees, K-means, Naïve bayes and neural network are used to evaluate the accuracy of occurrence of a heart disease. The experiments are carried out on heart data set of UCI machine learning repository and it is implemented on MATLAB. © 2011 IEEE.

关键词： data mining

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Identifying students' inquiry planning using machine learning

Identifying students' inquiry planning using machine learnin...

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3rd international conference on Educational data mining, EDM 2010

作者： Montalvo, Orlando Baker, Ryan S.J.D. Pedro, Michael A. Sao Nakama, Adam Gobert, Janice D. Computer Science Department Worcester Polytechnic Institute United States Social Science and Policy Studies Department Worcester Polytechnic Institute United States

ISBN: (纸本)9780615375298

This research investigates the detection of student meta-cognitive planning processes in real-time using log tracing techniques. We use fine and coarse-grained data distillation, in combination with coarse-grained text replay coding, in order to develop detectors for students' planning of experiments in Science Assistments, an assessment and tutoring system for scientific inquiry. The goal is to recognize student inquiry planning behavior in real-time as the student conducts inquiry in a micro-world;the eventual goal is to provide real-time scaffolding of scientific inquiry.

关键词： Students

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