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Distributed and shared memory algorithm for parallel mining of association rules 1

引用

5th international conference on machine learning and data mining in pattern recognition, MLDM 2007

作者： Palancar, J. Hernández Tormo, O. Fraxedas Cárdenas, J. Festán Leán, R. Hernández 7a # 21812 e/ 218 y 222 Rpto. Siboney Playa La Habana Cuba

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

the search for frequent patterns in transactional databases is considered one of the most important data mining problems. Several parallel and sequential algorithms have been proposed in the literature to solve this problem. Almost all of these algorithms make repeated passes over the dataset to determine the set of frequent itemsets, thus implying high I/O overhead. In the parallel case, most algorithms perform a sum-reduction at the end of each pass to construct the global counts, also implying high synchronization cost. We present a novel algorithm that exploits efficiently the trade-offs between computation, communication, memory usage and synchronization. the algorithm was implemented over a cluster of SMP nodes combining distributed and shared memory paradigms. this paper presents the results of our algorithm on different data sizes experimented on different numbers of processors, and studies the effect of these variations on the overall performance. © Springer-Verlag Berlin Heidelberg 2007.

关键词： data mining

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Dynamic distance-based active learning with SVM 1

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5th international conference on machine learning and data mining in pattern recognition, MLDM 2007

作者： Jiang, Jun Ip, Horace H. S. Image Computing Group Department of Computer Science Hong Kong City University of Hong Kong Hong Kong Hong Kong

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

In this paper, we present a novel active learning strategy, named dynamic active learning with SVM to improve the effectiveness of learning sample selection in active learning. the algorithm is divided into two steps. the first step is similar to the standard distance-based active learning with SVM [1] in which the sample nearest to the decision boundary is chosen to induce a hyperplane that can halve the current version space. In order to improve upon the learning efficiency and convergent rates, we propose in the second step, a dynamic sample selection strategy that operates within the neighborhood of the "standard" sample. theoretical analysis is given to show that our algorithm will converge faster than the standard distance-based technique and using less number of samples while maintaining the same classification precision rate. We also demonstrate the feasibility of the dynamic selection strategy approach through conducting experiments on several benchmark datasets. © Springer-Verlag Berlin Heidelberg 2007.

关键词： learning algorithms

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Variable Width Elliptic Gaussian Kernels for Probability Density Estimation

Variable Width Elliptic Gaussian Kernels for Probability Den...

引用

international conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service (TELSIKS)

作者： Dragoljub Pokrajac Longin Jan Latecki Aleksandar Lazarevic Jelena Nikolic Computer and Information Science Department Applied Mathematics Research Center and Center for Research and Education in Optical Sciences and Applications (CREOSA) Delaware State University Dover DE USA CIS Department Temple University Philadelphia PA USA CREOSA and United Technologies Research Center Hartford CT USA Faculty of Electronic Engineering University of Nis Nis Serbia

Estimation of probability density functions based on available data is important problem arising in various fields, such as telecommunications, machine learning, data mining, pattern recognition and computer vision. In this paper, we consider Kernel-based non-parametric density estimation methods and derive formulae for variable kernel density estimation using generalized, elliptic Gaussian kernels. the proposed technique is verified on simulated data.

关键词： Kernel Probability density function machine learning data mining pattern recognition Computer vision Eigenvalues and eigenfunctions Random variables State estimation Optical computing

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Deciding the Convex Separability of pattern Sets

Deciding the Convex Separability of Pattern Sets

引用

IEEE international Workshop on Intelligent data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Gabor Takacs Bela Pataki Department of Measurement and Information Systems Budapest University슠of슠Technology슠and슠Economics Budapest Hungary

Deciding the convex separability of the classes is an interesting question in the data exploration phase of building classification systems. In this paper we propose an efficient algorithm for deciding the convex separability of two point sets in R d . We compare our algorithm with conventional methods on 6 benchmark problems, and demonstrate that our algorithm is significantly faster.

关键词： machine learning algorithms Polynomials conferences data acquisition Economic forecasting Information systems pattern recognition machine learning Intelligent systems Intelligent structures

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A Novel Ensemble Strategy for Classification of Prostate Cancer Protein Mass Spectra

A Novel Ensemble Strategy for Classification of Prostate Can...

引用

29th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), vol.15

作者： Amin Assareh Mohammad Hassan Moradi Vahid Esmaeili Biomedical Engineering Amirkabir University of Technology슠 Tehran Iran Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Iran

Protein mass spectra pattern recognition is a new forum, in which many machine learning algorithms have been conducted to enhance the chance of early cancer diagnosis. the high-dimensionality-small-sample (HDSS) problem of cancer proteomic datasets still requires more sophisticated approaches to improve the classification accuracy. In this study we present a simple ensemble strategy based on measuring the generalizing capability of different subsets of training data and apply it in making final decision. Using a limited number of biomarkers along with 5 classification algorithms, the proposed method achieved a promising performance over a well-known prostate cancer mass spectroscopy dataset.

关键词： Prostate cancer Proteins machine learning algorithms Proteomics Biomarkers Cancer detection Classification algorithms Mass spectroscopy data mining pattern recognition

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Advances in Neural Networks - ISNN 2007 2007

引用

丛书名： Lecture Notes in Computer Science

2007年

作者： Derong Liu Shumin Fei Zeng-Guang Hou Huaguang Zhang Changyin Sun

ISBN: (数字)9783540723837

ISBN: (纸本)9783540723820

the three volume set LNCS 4491/4492/4493 constitutes the refereed proceedings of the 4th international Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. the 262 revised long papers and 192 revised short papers presented were carefully reviewed and selected from a total of 1.975 submissions. the papers are organized in topical sections on neural fuzzy control, neural networks for control applications, adaptive dynamic programming and reinforcement learning, neural networks for nonlinear systems modeling, robotics, stability analysis of neural networks, learning and approximation, data mining and feature extraction, chaos and synchronization, neural fuzzy systems, training and learning algorithms for neural networks, neural network structures, neural networks for pattern recognition, SOMs, ICA/PCA, biomedical applications, feedforward neural networks, recurrent neural networks, neural networks for optimization, support vector machines, fault diagnosis/detection, communications and signal processing, image/video processing, and applications of neural networks.

关键词： Neural networks (Computer science) Congresses. Neural computers Congresses.

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Toward Building Lightweight Intrusion Detection System through Modified RMHC and SVM

Toward Building Lightweight Intrusion Detection System Throu...

引用

IEEE international conference on Networks

作者： You Chen Wen-Fa Li Xue-Qi Cheng Graduate University Chinese Academy and Sciences China Chinese Academy of Sciences Beijing Beijing CN Institute of Computing Technology Chinese Academy and Sciences China

ISBN: (纸本)9781424412297;1424412293

Feature selection attracted much interest from researchers in many fields such as network security, pattern recognition and data mining. In this paper, we present a wrapper-based feature selection algorithm aiming at modeling lightweight intrusion detection system (IDS) by (1) using modified random mutation hill climbing (MRMHC) as search strategy to specify a candidate subset for evaluation; (2) using support vector machines (SVMs) as wrapper approach to obtain the optimum feature subset. We have examined the feasibility of our feature selection algorithm by conducting several experiments on KDD 1999 intrusion detection dataset which was categorized as DOS, PROBE, R2L and U2R. the experimental results show that our approach is able not only to speed up the process of selecting important features but also to guarantee high detection rates. Furthermore, our experiments indicate that intrusion detection system with a combination of our proposed approach has smaller computational resources than that with GA-SVM which is a popular feature selection algorithm in the field.

关键词： Intrusion detection Support vector machines Filters Computational complexity machine learning algorithms Genetic algorithms Support vector machine classification Accuracy Computers Optimization methods

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Performance comparison of pattern discovery methods on web log data

Performance comparison of pattern discovery methods on web l...

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4th IEEE/ACS international conference on Computer Systems and Applications (AICCSA-06)

作者： Bayir, Murat Ali Toroslu, Ismail H. Cosar, Ahmet Middle East Tech Univ Dept Comp Engn TR-06531 Ankara Turkey

ISBN: (纸本)1424402115

One of the popular trends in computer science has been development of intelligent web-based systems. Demand for such systems forces designers to make use of knowledge discovery techniques on web server logs. Web usage mining has become a major area of knowledge discovery on World Wide Web. Frequent pattern discovery is one of the main issues in web usage mining. these frequent patterns constitute the basic information source for intelligent web-based systems. In this paper;frequent pattern mining algorithms for web log data and their performance comparisons are examined. Our study is mainly focused on finding suitable pattern mining algorithms for web server logs.

关键词： pattern recognition

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An efficient algorithm for incremental mining of sequential patterns

An efficient algorithm for incremental mining of sequential ...

引用

4th international conference on machine learning and Cybernetics

作者： Ren, Jia-Dong Zhou, Xiao-Lei Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China

ISBN: (纸本)3540335846

mining of sequential patterns is an important issue among the various data mining problems. the problem of incremental mining of sequential patterns deserves as much attention. In this paper, we consider the problem of the incremental updating of sequential pattern mining when some transactions and/or data sequences are deleted from the original sequence database. We present a new algorithm, called IU_D, for mining frequent sequences so as to make full use of information obtained during an earlier mining process for reducing the cost of finding new sequential patterns in the updated database. the results of our experiment show that the algorithm performs significantly faster than the naive approach of mining the entire updated database from scratch.

关键词： data mining

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Improving nearest neighbor classifier using Tabu Search and ensemble distance metrics

Improving nearest neighbor classifier using Tabu Search and ...

引用

6th IEEE international conference on data mining

作者： Tahir, Muhammad Atif Smith, James Univ W England Sch Comp Sci Bristol BS16 1QY Avon England

ISBN: (纸本)9780769527017

the nearest-neighbor (NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good results with this technique is the choice of distance function, and correspondingly which features to consider when computing distances between samples. In this paper a new ensemble technique is proposed to improve the performance of NN classifier the proposed approach combines multiple NN classifiers, where each classifier uses a different distance function and potentially a different set of features (feature vector). these feature vectors are determined for each distance metric using Simple Voting Scheme incorporated in Tabu Search (TS). the proposed ensemble classifier with different distance metrics and different feature vectors (TS-DF/NN) is evaluated using various benchmark data sets from UCI machine learning Repository. Results have indicated a significant increase in the performance when compared with various well-known classifiers. Furthermore, the proposed ensemble method is also compared with ensemble classifier using different distance metrics but with same feature vector (with or without Feature Selection (FS)).

关键词： pattern recognition

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