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An A-team approach to learning classifiers from distributed data sources

2nd KES International Symposium on Agent and Multi-Agent Systems

作者： Czarnowski, Ireneusz Jedrzejowicz, Piotr Wierzbowska, Izabela Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

ISBN: (纸本)9783540785811

distributed data mining is an important research area. The task of the distributed data mining is to analyze data from different sources. Solving such tasks requires a special approach and tools, different from those dedicated to learning from data located in a single database. This paper presents an approach to learning classifiers from distributed data based on data reduction (the prototype selection) at a local level. The problem is solved through applying the A-Team concept implemented using the JABAT environment, which supports implementation of multiple-agent teams. The paper includes a general overview of the JABAT, the problem formulation and some technical details of the proposed implementation. Finally, the computational experiment results validating the approach are shown.

关键词： multi-agent system learning classifiers distributed data mining

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An Iterative Hadoop-Based Ensemble data Classification Model on distributed Medical databases 1st

An Iterative Hadoop-Based Ensemble Data Classification Model...

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1st International Conference on Computational Intelligence and Informatics (ICCII)

作者： Bikku, Thulasi Nandam, Sambasiva Rao Akepogu, Ananda Rao VNITSW Dept CSE Guntur AP India SRITW Dept CSE Warangal Telangana India JNTUCEA Dept CSE Ananthapuramu India

ISBN: (纸本)9789811024719;9789811024702

As the size and complexity of the online biomedical databases are growing day by day, finding an essential structure or unstructured patterns in the distributed biomedical applications has become more complex. Traditional Hadoop-based distributed decision tree models such as Probability based decision tree (PDT), Classification And Regression Tree (CART) and Multiclass Classification Decision Tree have failed to discover relational patterns, user-specific patterns and feature-based patterns, due to the large number of feature sets. These models depend on selection of relevant attributes and uniform data distribution. data imbalance, indexing and sparsity are the three major issues in these distributed decision tree models. In this proposed model, an enhanced attributes selection ranking model and Hadoop-based decision tree model were implemented to extract the user-specific interesting patterns in online biomedical databases. Experimental results show that the proposed model has high true positive, high precision and low error rate compared to traditional distributed decision tree models.

关键词： distributed data mining Hadoop Ensemble approach Medical databases

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distributed Multi Class SVM for Large data Sets 15

Distributed Multi Class SVM for Large Data Sets

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3rd International Symposium on Women in Computing and Informatics (WCI)

作者： Govada, Aruna Gauri, Bhavul Sahay, S. K. BITS Pilani KK Birla Goa Campus Sancoale Goa India

ISBN: (纸本)9781450333610

data mining algorithms are originally designed by assuming the data is available at one centralized *** algorithms also assume that the whole data is fit into main memory while running the algorithm. But in today's scenario the data has to be handled is distributed even geographically. Bringing the data into a centralized site is a bottleneck in terms of the bandwidth when compared with the size of the data. In this paper for multiclass SVM we propose an algorithm which builds a global SVM model by merging the local SVMs using a distributed approach(DSVM). And the global SVM will be communicated to each site and made it available for further classification. The experimental analysis has shown promising results with better accuracy when compared with both the centralized and ensemble method. The time complexity is also reduced drastically because of the parallel construction of local SVMs. The experiments are conducted by considering the data sets of size 100s to hundred of 100s which also addresses the issue of scalability.

关键词： distributed data mining MultiClass SVM One-Vs-One(OVO)

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distributed Computation of Persistent Homology from Partitioned Big data

Distributed Computation of Persistent Homology from Partitio...

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IEEE International Conference on Cluster Computing (Cluster)

作者： Malott, Nicholas O. Verma, Rishi R. Singh, Rohit P. Wilsey, Philip A. Univ Cincinnati Dept EECS Cincinnati OH 45221 USA

ISBN: (纸本)9781728196664

Topological data Analysis is a machine learning method that summarizes the topological features of a space. Persistent Homology (PH) can identify these topological features as they persist within a point cloud;persisting in respect to the connectedness of the point cloud at increasing distances. The utility of PH is apparent in several fields including bioinformatics, network security, and object classification. However, the memory complexity of PH limits the application to relatively small point clouds for low-dimensional topological feature identification. For this reason, numerous approaches to optimize and approximate the PH have been introduced for providing results over large point clouds. One solution, Partitioned Persistent Homology (PPH), has shown favorable approximation on a single node with significant performance improvement. However, the single-node approach is limited by the available system memory, leading to the need for a distributed approach for additional (especially memory) resources. This paper studies a distributed version of PPH for use with large point clouds over a high-performance compute cluster. Experimental results of the distributed algorithm against previous studies is presented along with scalability of the distributed library.

关键词： topological data analysis persistent homology data partitioning distributed data mining distributed computing

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distributed LEARNING ALGORITHM BASED ON data REDUCTION

DISTRIBUTED LEARNING ALGORITHM BASED ON DATA REDUCTION

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1st International Conference on Agents and Artificial Intelligence

作者： Czarnowski, Ireneusz Jedrzejowicz, Piotr Gdynia Maritime Univ Dept Informat Syst Morska 83 PL-81225 Gdynia Poland

ISBN: (纸本)9789898111661

s The paper presents an approach to learning classifiers from distributed data, based on a data reduction at a local level. In such case, the aim of data reduction is to obtain a compact representation of distributed data repositories, that include non-redundant information in the form of so-called prototypes. In the paper data reduction is carried out by simultaneously selecting instances and features, finally producing prototypes which do not have to be homogenous and can include different sets of features. From these prototypes the global classifier based on a feature voting is constructed. To evaluate and compare the proposed approach computational experiment was carried out. The experiment results indicate that data reduction at the local level and next merger of prototypes into the global classifier can produce very good classification results.

关键词： distributed data mining distributed learning classifiers data reduction Agent-based approach

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Training Normal Bayes Classifier on distributed data 13

Training Normal Bayes Classifier on Distributed Data

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13th International Symposium on Intelligent Systems (INTELS)

作者： Kholod, I. I. Kuprianov, M. S. Titkov, E. V. Shorov, A. V. Postnikov, E. V. Mironenko, I. G. Sokolov, S. S. St Petersburg Electrotech Univ LETI Prof Popova Str 5 St Petersburg 197376 Russia

The paper describes an approach to parallelization of Normal Bayes classifier training algorithm for distributed data. In the process of distributed data analysis and the algorithm performance, the results fail to join properly. Due to this, the algorithm is to be performed in a distributed manner For this purpose, we use representation of the algorithm as a sequential composition of functions. The algorithm is parallelized to work with data distributed horizontally and vertically. This allows placing parallel functions of the algorithm at data nodes. Experiments show that transfer of computations to sources allow to decrease training time and network traffic. We implement the algorithm variants as an extension of the industrial-strength Java-based library Xelopes. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： data mining normal bayes classifier distributed data mining distributed data Big data

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Training Normal Bayes Classifier on distributed data

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Procedia Computer Science 2019年 150卷 389-396页

作者： I.I. Kholod M.S. Kuprianov E.V. Titkov A.V. Shorov E.V. Postnikov I.G. Mironenko S.S. Sokolov Saint Petersburg Electrotechnical University "LETI" Professora Popova str. 5 Saint Petersburg 197376 Russian Federation

The paper describes an approach to parallelization of Normal Bayes classifier training algorithm for distributed data. In the process of distributed data analysis and the algorithm performance, the results fail to join properly. Due to this, the algorithm is to be performed in a distributed manner. For this purpose, we use representation of the algorithm as a sequential composition of functions. The algorithm is parallelized to work with data distributed horizontally and vertically. This allows placing parallel functions of the algorithm at data nodes. Experiments show that transfer of computations to sources allow to decrease training time and network traffic. We implement the algorithm variants as an extension of the industrial-strength Java-based library Xelopes.

关键词： data mining normal bayes classifier distributed data mining distributed data Big data

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RACHET: An efficient cover-based merging of clustering hierarchies from distributed datasets

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distributed AND PARALLEL dataBASES 2002年第2期11卷 157-180页

作者： Samatova, NF Ostrouchov, G Geist, A Melechko, AV Oak Ridge Natl Lab Div Math & Comp Sci Oak Ridge TN 37831 USA Oak Ridge Natl Lab Mol Scale Engn & Nanoscale Technol Grp Oak Ridge TN 37831 USA

This paper presents a hierarchical clustering method named RACHET (Recursive Agglomeration of Clustering Hierarchies by Encircling Tactic) for analyzing multi-dimensional distributed data. A typical clustering algorithm requires bringing all the data in a centralized warehouse. This results in O(nd) transmission cost, where n is the number of data points and d is the number of dimensions. For large datasets, this is prohibitively expensive. In contrast, RACHET runs with at most O(n) time, space, and communication costs to build a global hierarchy of comparable clustering quality by merging locally generated clustering hierarchies. RACHET employs the encircling tactic in which the merges at each stage are chosen so as to minimize the volume of a covering hypersphere. For each cluster centroid, RACHET maintains descriptive statistics of constant complexity to enable these choices. RACHET's framework is applicable to a wide class of centroid-based hierarchical clustering algorithms, such as centroid, medoid, and Ward.

关键词： clustering distributed datasets distributed data mining

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A Reliable Application of MPC for Securing the Tri-Training Algorithm

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IEEE ACCESS 2023年 11卷 34718-34735页

作者： Kurniawan, Hendra Mambo, Masahiro Kanazawa Univ Grad Sch Nat Sci & Technol Kanazawa 9201192 Japan Kanazawa Univ Inst Sci & Engn Kanazawa 9201192 Japan

Due to the widespread use of distributed data mining techniques in a variety of areas, the issue of protecting the privacy of sensitive data has received increasing attention in recent years. Privacy-preserving distributed data mining (PPDDM) focuses on decentralized data analysis without the disclosure of sensitive information from data owner. However, the previous PPDDM mostly works on a limited amount of labeled data. In contrast to the real world, unlabeled data is abundance and labeled data is scarce. The objectives of this paper are to study and to analyze privacy-preserving properties of semi-supervised learning (SSL) algorithm with the combination of labeled and unlabeled data, where data is distributed among multiple data owners. In this paper we propose a Privacy-preserving distributed data mining (PPDDM) method by designing a reliable application of secure MPC to semi-supervised tri-training algorithms. We simulate the original tri-training algorithm and tri-training algorithm with secure MPC using a different types of classifiers and datasets. The simulation results show that tri-training in secure MPC has almost same accuracy compared to original tri-training algorithm. We also compare execution time in addition to performance evaluation of tri-training in secure and the original tri-training algorithms.

关键词： data models distributed databases data privacy Classification algorithms Computational modeling Semisupervised learning data mining distributed data mining multi-party computation privacy-preserving semi-supervised learning tri-training

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Efficient mining of association rules in distributed databases

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 1996年第6期8卷 911-922页

作者： Cheung, DW Ng, VT Fu, AW Fu, YJ HONG KONG POLYTECH UNIV DEPT COMP HONG KONG HONG KONG CHINESE UNIV HONG KONG DEPT COMP SCI & ENGN HONG KONG HONG KONG SIMON FRASER UNIV SCH COMP SCI BURNABY BC V5A 1S6 CANADA

Many sequential algorithms have been proposed for mining of association rules. However, very little work has been done in mining association rules in distributed databases. A direct application of sequential algorithms to distributed databases is not effective, because it requires a large amount of communication overhead. In this study, an efficient algorithm, DMA, is proposed. It generates a small number of candidate sets and requires only O(n) messages for support count exchange for each candidate set, where n is the number of sites in a distributed database. The algorithm has been implemented on an experimental test bed and its performance is studied. The results show that DMA has superior performance when comparing with the direct application of a popular sequential algorithm in distributed databases.

关键词： data mining knowledge discovery distributed data mining association rule distributed database distributed algorithm partitioned database

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