相关论文
2009 - Neurocomputing
Localized support vector regression for time series prediction

Time series prediction, especially financial time series prediction, is a challenging task in machine learning. In this issue, the data are usually non-stationary and volatile in nature. Because of its good generalization power, the support vector regression (SVR) has been widely applied in this application. The standard SVR employs a fixed @e-tube to tolerate noise and adopts the @?"p-norm (p=1 or 2) to model the functional complexity of the whole data set. One problem of the standard SVR is that it considers data in a global fashion only. Therefore it may lack the flexibility to capture the local trend of data; this is a critical aspect of volatile data, especially financial time series data. Aiming to attack this issue, we propose the localized support vector regression (LSVR) model. This novel model is demonstrated to provide a systematic and automatic scheme to adapt the margin locally and flexibly; while the margin in the standard SVR is fixed globally. Therefore, the LSVR can tolerate noise adaptively. The proposed LSVR is promising in the sense that it not only captures the local information in data, but more importantly, it establishes connection with several models. More specifically: (1) it can be regarded as the regression extension of a recently proposed promising classification model, the Maxi-Min Margin Machine; (2) it incorporates the standard SVR as a special case under certain mild assumptions. We provide both theoretical justifications and empirical evaluations for this novel model. The experimental results on synthetic data and real financial data demonstrate its advantages over the standard SVR.

2010 - 2010 IEEE International Conference on Data Mining
Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs

Many time series data mining problems require subsequence similarity search as a subroutine. Dozens of similarity/distance measures have been proposed in the last decade and there is increasing evidence that Dynamic Time Warping (DTW) is the best measure across a wide range of domains. Given DTW’s usefulness and ubiquity, there has been a large community-wide effort to mitigate its relative lethargy. Proposed speedup techniques include early abandoning strategies, lower-bound based pruning, indexing and embedding. In this work we argue that we are now close to exhausting all possible speedup from software, and that we must turn to hardware-based solutions. With this motivation, we investigate both GPU (Graphics Processing Unit) and FPGA (Field Programmable Gate Array) based acceleration of subsequence similarity search under the DTW measure. As we shall show, our novel algorithms allow GPUs to achieve two orders of magnitude speedup and FPGAs to produce four orders of magnitude speedup. We conduct detailed case studies on the classification of astronomical observations and demonstrate that our ideas allow us to tackle problems that would be untenable otherwise.

2010 - Expert Syst. Appl.
Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods

Predicting business failure is a very critical task for government officials, stock holders, managers, employees, investors and researchers, especially in nowadays competitive economic environment. Several top 10 data mining methods have become very popular alternatives in business failure prediction (BFP), e.g., support vector machine and k nearest neighbor. In comparison with the other classification mining methods, advantages of classification and regression tree (CART) methods include: simplicity of results, easy implementation, nonlinear estimation, being non-parametric, accuracy and stable. However, there are seldom researches in the area of BFP that witness the applicability of CART, another method among the top 10 algorithms in data mining. The aim of this research is to explore the performance of BFP using the commonly discussed data mining technique of CART. To demonstrate the effectiveness of BFP using CART, business failure predicting tasks were performed on the data set collected from companies listed in the Shanghai Stock Exchange and Shenzhen Stock Exchange. Thirty times' hold-out method was employed as the assessment, and the two commonly used methods in the top 10 data mining algorithms, i.e., support vector machine and k nearest neighbor, and the two baseline benchmark methods from statistic area, i.e., multiple discriminant analysis (MDA) and logistics regression, were employed as comparative methods. For comparative methods, stepwise method of MDA was employed to select optimal feature subset. Empirical results indicated that the optimal algorithm of CART outperforms all the comparative methods in terms of predictive performance and significance test in short-term BFP of Chinese listed companies.

2018 - Healthcare
A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining

The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However, the lack of a comprehensive and systematic narrative motivated us to construct a literature review on this topic. In this paper, we present a review of the literature on healthcare analytics using data mining and big data. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a database search between 2005 and 2016. Critical elements of the selected studies—healthcare sub-areas, data mining techniques, types of analytics, data, and data sources—were extracted to provide a systematic view of development in this field and possible future directions. We found that the existing literature mostly examines analytics in clinical and administrative decision-making. Use of human-generated data is predominant considering the wide adoption of Electronic Medical Record in clinical care. However, analytics based on website and social media data has been increasing in recent years. Lack of prescriptive analytics in practice and integration of domain expert knowledge in the decision-making process emphasizes the necessity of future research.

2001
Data Mining and Uncertain Reasoning: An Integrated Approach

From the Publisher: Data Mining and Uncertain Reasoning is a practical reference for practitioners in various interrelated fields. Each subject is treated with both basic introductory and advanced technical descriptions, making the book suitable for students and practitioners at various levels of experience.

2014
Survey on Privacy Preserving Data Mining

Data mining is the process of extraction of data from large database. One of the most important topics in research community is Privacy preserving data mining (PPDM). Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. It is essential to maintain a ratio between privacy protection and knowledge discovery. To solve such problems there are some algorithms presented by various authors worldwide. The primary goal of this survey paper is to understand the existing privacy preserving data mining techniques and to achieve efficiency.

2020 - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Educational data mining and learning analytics: An updated survey

This survey is an updated and improved version of the previous one published in 2013 in this journal with the title “data mining in education”. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data‐Driven Education, Data‐Driven Decision‐Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.

2005 - IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
Evolutionary optimization of radial basis function classifiers for data mining applications

In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

2008 - Knowl. Based Syst.
Data mining method for listed companies' financial distress prediction

Data mining technique is capable of mining valuable knowledge from large and changeable database. This paper puts forward a data mining method combining attribute-oriented induction, information gain, and decision tree, which is suitable for preprocessing financial data and constructing decision tree model for financial distress prediction. On the base of financial ratios attributes and one class attribute, adopting entropy-based discretization method, a data mining model for listed companies' financial distress prediction is designed. The empirical experiment with 35 financial ratios and 135 pairs of listed companies as initial samples got satisfying result, which testifies the feasibility and validity of the proposed data mining method for listed companies' financial distress prediction.

2006
PREDICTING BREAST CANCER SURVIVABILITY USING DATA MINING TECHNIQUES

In this paper we present an analysis of the prediction of survivability rate of breast cancer patients using data mining techniques. The data used is the SEER Public-Use Data. The preprocessed data set consists of 151,886 records, which have all the available 16 fields from the SEER database. We have investigated three data mining techniques: the Naive Bayes, the back-propagated neural network, and the C4.5 decision tree algorithms. Several experiments were conducted using these algorithms. The achieved prediction performances are comparable to existing techniques. However, we found out that C4.5 algorithm has a much better performance than the other two techniques.

1998
Data Mining Solutions: Methods and Tools for Solving Real-World Problems

DEFINING THE DATA MINING APPROACH. What is Data Mining? Understanding Data Modeling. Defining the Problems to be Solved. DATA PREPARATION AND ANALYSIS. Accessing and Preparing the Data. Visual Methods for Analyzing Data. Nonvisual Analytical Methods. ASSESSING DATA MINING TOOLS AND TECHNOLOGIES. Link Analysis Tools. Landscape Visualization Tools. Quantitative Data Mining Tools. Future Trends in Visual Data Mining. CASE STUDIES. Mapping the Human Genome. Telecommunication Services. Banking and Finance. Retail Data Mining. Financial Market Data Mining. Money Laundering and Other Financial Crimes. Appendix. What's on the CD--ROM. Index.

1999 - WEBKDD
Discovery of Interesting Usage Patterns from Web Data

Web Usage Mining is the application of data mining techniques to large Web data repositories in order to extract usage patterns. As with many data mining application domains, the identification of patterns that are considered interesting is a problem that must be solved in addition to simply generating them. Aneces sary step in identifying interesting results is quantifying what is considered uninteresting in order to form a basis for comparison. Several research efforts have relied on manually generated sets of uninteresting rules. However, manual generation of a comprehensive set of evidence about beliefs for a particular domain is impractical in many cases. Generally, domain knowledge can be used to automatically create evidence for or against a set of beliefs. This paper develops a quantitative model based on support logic for determining the interestingness of discovered patterns. For Web Usage Mining, there are three types of domain information available; usage, content, and structure. This paper also describes algorithms for using these three types of information to automatically identify interesting knowledge. These algorithms have been incorporated into the Web Site Information Filter (WebSIFT) system and examples of interesting frequent itemsets automatically discovered from real Web data are presented.

2007 - 2007 IEEE Conference on Computer Vision and Pattern Recognition
Feature Mining for Image Classification

The efficiency and robustness of a vision system is often largely determined by the quality of the image features available to it. In data mining, one typically works with immense volumes of raw data, which demands effective algorithms to explore the data space. In analogy to data mining, the space of meaningful features for image analysis is also quite vast. Recently, the challenges associated with these problem areas have become more tractable through progress made in machine learning and concerted research effort in manual feature design by domain experts. In this paper, we propose a feature mining paradigm for image classification and examine several feature mining strategies. We also derive a principled approach for dealing with features with varying computational demands. Our goal is to alleviate the burden of manual feature design, which is a key problem in computer vision and machine learning. We include an in-depth empirical study on three typical data sets and offer theoretical explanations for the performance of various feature mining strategies. As a final confirmation of our ideas, we show results of a system, that utilizing feature mining strategies matches or outperforms the best reported results on pedestrian classification (where considerable effort has been devoted to expert feature design).

2007
Meta-learning in distributed data mining systems: Issues and approaches

Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach to this objective is to apply various machine learning algorithms to compute descriptive models of the available data. Here, we explore one of the main challenges in this research area, the development of techniques that scale up to large and possibly physically distributed databases. Meta-learning is a technique that seeks to compute higher-level classifiers (or classification models), called meta-classifiers, that integrate in some principled fashion multiple classifiers computed separately over different databases. This study, describes meta-learning and presents the JAM system (Java Agents for Meta-learning), an agent-based meta-learning system for large-scale data mining applications. Specifically, it identifies and addresses several important desiderata for distributed data mining systems that stem from their additional complexity compared to centralized or host-based systems. Distributed systems may need to deal with heterogenous platforms, with multiple databases and (possibly) different schemas, with the design and implementation of scalable and effective protocols for communicating among the data sites, and the selective and efficient use of the information that is gathered from other peer data sites. Other important problems, intrinsic within ∗Supported in part by an IBM fellowship. data mining systems that must not be ignored, include, first, the ability to take advantage of newly acquired information that was not previously available when models were computed and combine it with existing models, and second, the flexibility to incorporate new machine learning methods and data mining technologies. We explore these issues within the context of JAM and evaluate various proposed solutions through extensive empirical studies.

2015 - International Journal of Electrical and Computer Engineering
Comparing Performance of Data Mining Algorithms in Prediction Heart Diseases

Heart diseases are among the nation’s leading couse of mortality and moribidity. Data mining teqniques can predict the likelihood of patients getting a heart disease. The purpose of this study is comparison of different data mining algorithm on prediction of heart diseases. This work applied and compared data mining techniques to predict the risk of heart diseases. After feature analysis, models by five algorithms including decision tree (C5.0), neural network, support vector machine (SVM), logistic regression and k-nearest neighborhood (KNN) were developed and validated. C5.0 Decision tree has been able to build a model with greatest accuracy 93.02%, KNN, SVM, Neural network have been 88.37%, 86.05% and 80.23% respectively. Produced results of decision tree can be simply interpretable and applicable; their rules can be understood easily by different clinical practitioner.

2002
Data Mining for Network Intrusion Detection

This paper gives an overview of our research in building rare class prediction models for identifying known intrusions and their variations and anomaly/outlier detection schemes for detecting novel attacks whose nature is unknown. Experimental results on the KDDCup’99 data set have demonstrated that our rare class predictive models are much more efficient in the detection of intrusive behavior than standard classification techniques. Experimental results on the DARPA 1998 data set, as well as on live network traffic at the University of Minnesota, show that the new techniques show great promise in detecting novel intrusions. In particular, during the past few months our techniques have been successful in automatically identifying several novel intrusions that could not be detected using state-of-the-art tools such as SNORT. In fact, many of these have been on the CERT/CC list of recent advisories and incident notes.

2001 - MDM/KDD
Application of Data Mining Techniques for Medical Image Classification

Breast cancer represents the second leading cause of cancer deaths in women today and it is the most common type of cancer in women. This paper presents some experiments for tumour detection in digital mammography. We investigate the use of different data mining techniques, neural networks and association rule mining, for anomaly detection and classification. The results show that the two approaches performed well, obtaining a classification accuracy reaching over 70% percent for both techniques. Moreover, the experiments we conducted demonstrate the use and effectiveness of association rule mining in image categorization.

2016
Data Mining Practical Machine Learning Tools And Techniques With Java Implementations

Thank you for reading data mining practical machine learning tools and techniques with java implementations. As you may know, people have look hundreds times for their favorite novels like this data mining practical machine learning tools and techniques with java implementations, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their laptop.

1998 - KDD
Data Mining for Direct Marketing: Problems and Solutions

Direct marketing is a process of identifying likely buyers of certain products and promoting the products accordingly. It is increasingly used by banks, insurance companies, and the retail industry. Data mining can provide an effective tool for direct marketing. During data mining, several specific problems arise. For example, the class distribution is extremely unbalanced (the response rate is about 1%), the predictive accuracy is no longer suitable for evaluating learning methods, and the number of examples can be too large. In this paper, we discuss methods of coping with these problems based on our experience on direct-marketing projects using data mining.

2009 - Expert Syst. Appl.
Application of data mining techniques in customer relationship management: A literature review and classification

Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000-2006 covering 24 journals and proposes a classification scheme to classify the articles. Nine hundred articles were identified and reviewed for their direct relevance to applying data mining techniques to CRM. Eighty-seven articles were subsequently selected, reviewed and classified. Each of the 87 selected papers was categorized on four CRM dimensions (Customer Identification, Customer Attraction, Customer Retention and Customer Development) and seven data mining functions (Association, Classification, Clustering, Forecasting, Regression, Sequence Discovery and Visualization). Papers were further classified into nine sub-categories of CRM elements under different data mining techniques based on the major focus of each paper. The review and classification process was independently verified. Findings of this paper indicate that the research area of customer retention received most research attention. Of these, most are related to one-to-one marketing and loyalty programs respectively. On the other hand, classification and association models are the two commonly used models for data mining in CRM. Our analysis provides a roadmap to guide future research and facilitate knowledge accumulation and creation concerning the application of data mining techniques in CRM.

Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping

Eamonn J. Keogh
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Qiang Zhu
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Gustavo E. A. P. A. Batista
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M. Brandon Westover
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Abdullah Mueen
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Jesin Zakaria
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Thanawin Rakthanmanon
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Bilson J. L. Campana
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A. Mueen
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Qiang Zhu
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T. Rakthanmanon
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M. Westover
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J. Zakaria

Abstract:Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. The difficulty of scaling search to large datasets largely explains why most academic work on time series data mining has plateaued at considering a few millions of time series objects, while much of industry and science sits on billions of time series objects waiting to be explored. In this work we show that by using a combination of four novel ideas we can search and mine truly massive time series for the first time. We demonstrate the following extremely unintuitive fact; in large datasets we can exactly search under DTW much more quickly than the current state-of-the-art Euclidean distance search algorithms. We demonstrate our work on the largest set of time series experiments ever attempted. In particular, the largest dataset we consider is larger than the combined size of all of the time series datasets considered in all data mining papers ever published. We show that our ideas allow us to solve higher-level time series data mining problem such as motif discovery and clustering at scales that would otherwise be untenable. In addition to mining massive datasets, we will show that our ideas also have implications for real-time monitoring of data streams, allowing us to handle much faster arrival rates and/or use cheaper and lower powered devices than are currently possible.

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引用
Ecosystem on the Web : Non-Linear Dynamical Systems for Online Social Activities
2017
Knowledge Science, Engineering and Management: 13th International Conference, KSEM 2020, Hangzhou, China, August 28–30, 2020, Proceedings, Part I
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2020
Effective Sub-Sequence-Based Dynamic Time Warping
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2019
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2018
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2020
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2020
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2015
Behavior Analysis for Electronic Commerce Trading Systems: A Survey
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2019
Mining Linguistic Tone Patterns Using Fundamental Frequency Time-Series Data
2017
Fast trajectory search for real-world applications
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Structural health monitoring meets data mining
2014
Design of Management Platform Architecture and Key Algorithm for Massive Monitoring Big Data
Wireless Communications and Mobile Computing
2021
Codebook-based electrooculography data analysis towards cognitive activity recognition
Comput. Biol. Medicine
2018
Cryptomining Cannot Change Its Spots: Detecting Covert Cryptomining Using Magnetic Side-Channel
IEEE Transactions on Information Forensics and Security
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Scaling learning algorithms using locality sensitive hashing
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Profiling spatial and temporal behaviour in sensor networks: A case study in energy monitoring
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Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones
Sensors
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Prefix and Suffix Invariant Dynamic Time Warping
2016 IEEE 16th International Conference on Data Mining (ICDM)
2016