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distributed volunteer computing for solving ensemble learning problems

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2016年 54卷 68-78页

作者： Cesario, Eugenio Mastroianni, Carlo Talia, Domenico ICAR CNR I-87036 Arcavacata Di Rende CS Italy

The volunteer computing paradigm, along with the tailored use of peer-to-peer communication, has recently proven capable of solving a wide area of data-intensive problems in a distributed scenario. The mining@HOME framework is based on these paradigms and it has been implemented to run a wide range of distributed data mining applications. The efficiency and scalability of the architecture can be fully exploited when the overall task can be partitioned into distinct jobs that may be executed in parallel, and input data can be reused, which naturally leads to the use of data cachers. This paper explores the opportunities offered by mining@HOME for coping with the discovery of classifiers through the use of the bagging approach: multiple learners are used to compute models from the same input data, so as to extract a final model with high statistical accuracy. Analysis focuses on the evaluation of experiments performed in a real distributed environment, enriched with simulation assessment - to evaluate very large environments - and with an analytical investigation based on the iso-efficiency methodology. An extensive set of experiments allowed to analyze a number of heterogeneous scenarios, with different problem sizes, which helps to improve the performance by appropriately tuning the number of workers and the number of interconnected domains. (c) 2015 Elsevier B.V. All rights reserved.

关键词： Ensemble learning distributed data mining Peer-to-peer Volunteer computing

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A distributed EM algorithm to estimate the parameters of a finite mixture of components

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KNOWLEDGE AND INFORMATION SYSTEMS 2010年第3期23卷 267-292页

作者： Safarinejadian, Behrooz Menhaj, Mohammad B. Karrari, Mehdi Amirkabir Univ Technol Dept Elect Engn Tehran Iran

In this paper, a distributed expectation maximization (DEM) algorithm is first introduced in a general form for estimating the parameters of a finite mixture of components. This algorithm is used for density estimation and clustering of data distributed over nodes of a network. Then, a distributed incremental EM algorithm (DIEM) with a higher convergence rate is proposed. After a full derivation of distributed EM algorithms, convergence of these algorithms is analyzed based on the negative free energy concept used in statistical physics. An analytical approach is also developed for evaluating the convergence rate of both incremental and distributed incremental EM algorithms. It is analytically shown that the convergence rate of DIEM is much faster than that of the DEM algorithm. Finally, simulation results approve that DIEM remarkably outperforms DEM for both synthetic and real data sets.

关键词： distributed data mining EM algorithm data clustering Density estimation

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A novel algorithm for mining association rules in Wireless Ad Hoc Sensor Networks

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IEEE TRANSACTIONS ON PARALLEL AND distributed SYSTEMS 2008年第7期19卷 865-877页

作者： Boukerche, Azzedine Samarah, Samer Univ Ottawa Sch Informat Technol & Engn Ottawa ON K1N 6N5 Canada Univ Ottawa PARADISE Res Lab Ottawa ON K1N 6N5 Canada

In this paper, we propose a comprehensive framework for mining Wireless Ad Hoc Sensor Networks (WASNs), which is able to extract patterns regarding the sensors' behaviors. The main goal of determining behavioral patterns is to use them to generate rules that will improve the WASN's Quality of Service by participating in the resource management process or compensating for the undesired side effects of wireless communication. The proposed framework consists of 1) a formal definition of sensor behavioral patterns and sensor association rules, 2) a novel representation structure that we refer to as the Positional Lexicographic Tree (PLT) that is able to compress the data gathered for the mining process and thus allows the fast and efficient mining of sensor behavioral patterns, and 3) a distributed data extraction mechanism to prepare the data required for mining sensor behavioral patterns. Several experimental studies have been conducted to evaluate our PLT structure and our proposed data extraction algorithms for mining wireless sensor networks.

关键词： wireless sensor networks distributed systems distributed data mining

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distributed parallel support vector machines in strongly connected networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2008年第7期19卷 1167-1178页

作者： Lu, Yumao Roychowdhury, Vwani Vandenberghe, Lieven Yahoo Inc Sunnyvale CA 94089 USA Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

In this paper, we propose a distributed parallel support vector machine (DPSVM) training mechanism in a configurable network environment for distributed data mining. The basic idea is to exchange support vectors among a strongly connected network (SCN) so that multiple servers may work concurrently on distributed data set with limited communication cost and fast training speed. The percentage of servers that can work in parallel and the communication overhead may be adjusted through network configuration. The proposed algorithm further speeds up through online implementation and synchronization. We prove that the global optimal classifier can be achieved iteratively over an SCN. Experiments on a real-world data set show that the computing time scales well with the size of the training data for most networks. Numerical results show that a randomly generated SCN may achieve better performance than the state of the art method, cascade SVM, in terms of total training time.

关键词： convergence distributed data mining parallel computing strongly connected networks (SCNs) support vector machine (SVM)

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Performance study of distributed Apriori-like frequent itemsets mining

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KNOWLEDGE AND INFORMATION SYSTEMS 2010年第1期23卷 55-72页

作者： Aouad, Lamine M. Le-Khac, Nhien-An Kechadi, Tahar M. Univ Coll Dublin Sch Comp Sci & Informat Dublin 4 Ireland

In this article, we focus on distributed Apriori-based frequent itemsets mining. We present a new distributed approach which takes into account inherent characteristics of this algorithm. We study the distribution aspect of this algorithm and give a comparison of the proposed approach with a classical Apriori-like distributed algorithm, using both analytical and experimental studies. We find that under a wide range of conditions and datasets, the performance of a distributed Apriori-like algorithm is not related to global strategies of pruning since the performance of the local Apriori generation is usually characterized by relatively high success rates of candidate sets frequency at low levels which switch to very low rates at some stage, and often drops to zero. This means that the intermediate communication steps and remote support counts computation and collection in classical distributed schemes are computationally inefficient locally, and then constrains the global performance. Our performance evaluation is done on a large cluster of workstations using the Condor system and its workflow manager DAGMan. The results show that the presented approach greatly enhances the performance and achieves good scalability compared to a typical distributed Apriori founded algorithm.

关键词： distributed data mining Frequent itemsets generation The Apriori algorithm Grid computing

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Argumentation based reinforcement learning for meta-knowledge extraction

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INFORMATION SCIENCES 2020年 506卷 258-272页

作者： Xu, Junyi Yao, Li Li, Le Ji, Ming Tang, Guoming Acad Mil Sci Ctr Assessment & Demonstrat Res Beijing Peoples R China Natl Univ Def Technol Coll Informat Syst & Management Changsha Hunan Peoples R China Cent Mil Commiss Audit Off Beijing Peoples R China

Knowledge integration in distributed data mining has received widespread attention that aims to integrate inconsistent information locating on distributed sites. Traditional integration methods become ineffective since they are unable to generate global knowledge, support advanced integration strategy, or make prediction without individual classifiers. In this paper, we propose an argumentation based reinforcement learning method to handle this problem. Inspired by meta-learning, we integrate distributed knowledge and extract meta-knowledge that is agreed-upon knowledge consistent to all the agents. Specifically, two learning stages are introduced: argumentation based learning stage integrates and extracts meta-knowledge, and reinforcement learning stage evaluates and refines meta-knowledge. The two learning stages run alternately to extract global meta-knowledge base, which can be used to make prediction directly. The results from extensive experiments demonstrate that our method can extract refined meta-knowledge with a much satisfied performance. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Argumentation Reinforcement learning Knowledge integration distributed data mining

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distributed stream clustering using micro-clusters on Apache Storm

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JOURNAL OF PARALLEL AND distributed COMPUTING 2017年 108卷 74-84页

作者： Karunaratne, Pasan Karunasekera, Shanika Harwood, Aaron Univ Melbourne Dept Comp & Informat Syst Parkville Vic 3010 Australia

The recent need to extract real-time insights from data has driven the need for machine learning algorithms that can operate on data streams. Given the current extreme rates of data generation (around 5000 messages per second), these algorithms need to be able to handle data streams of very high velocity. Many current algorithms do not reach this requirement, in some cases processing only tens of messages per second. In this work we address the problem of limited achievable throughput of stream clustering by developing scalable distributed algorithms based on the micro-clustering paradigm that run on cloud platforms. We present two distributed architectures to execute the algorithms in parallel and implement these architectures on the Apache Storm stream processing platform. We demonstrate that we are able to gain close to an order of magnitude of improvement of performance in our experiments. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Stream data mining distributed data mining Stream clustering

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Effects of resampling method and adaptation on clustering ensemble efficacy

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ARTIFICIAL INTELLIGENCE REVIEW 2014年第1期41卷 27-48页

作者： Minaei-Bidgoli, Behrouz Parvin, Hamid Alinejad-Rokny, Hamid Alizadeh, Hosein Punch, William F. Iran Univ Sci & Technol Dept Comp Engn Tehran Iran Islamic Azad Univ Sci & Res Branch Dept Comp Engn Tehran Iran Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

Clustering ensembles combine multiple partitions of data into a single clustering solution of better quality. Inspired by the success of supervised bagging and boosting algorithms, we propose non-adaptive and adaptive resampling schemes for the integration of multiple independent and dependent clusterings. We investigate the effectiveness of bagging techniques, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms. In our adaptive approach, individual partitions in the ensemble are sequentially generated by clustering specially selected subsamples of the given dataset. The sampling probability for each data point dynamically depends on the consistency of its previous assignments in the ensemble. New subsamples are then drawn to increasingly focus on the problematic regions of the input feature space. A measure of data point clustering consistency is therefore defined to guide this adaptation. Experimental results show improved stability and accuracy for clustering structures obtained via bootstrapping, subsampling, and adaptive techniques. A meaningful consensus partition for an entire set of data points emerges from multiple clusterings of bootstraps and subsamples. Subsamples of small size can reduce computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data. This empirical study also compares the performance of adaptive and non-adaptive clustering ensembles using different consensus functions on a number of datasets. By focusing attention on the data points with the least consistent clustering assignments, whether one can better approximate the inter-cluster boundaries or can at least create diversity in boundaries and this results in improving clustering accuracy and convergence speed as a function of the number of partitions in the ensemble. The comparison of adaptive and non-adaptive approaches is a new avenue for research, and this study hel

关键词： Clustering ensembles Consensus functions distributed data mining Bootstrap Subsampling Adaptive clustering

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distributed data Clustering in the Context of the Internet of Things: A data Traffic Reduction Approach 17

Distributed Data Clustering in the Context of the Internet o...

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Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web

作者： Ricardo de Azevedo Brandao Ronaldo Ribeiro Goldschmidt Instituto Militar de Engenharia Rio de Janeiro Brazil

ISBN: (纸本)9781450350969

The Internet of Things (IoT) emerged with the objective to integrate physical objects into classical computer networks. These objects usually generate larges amount of data, transferring the bottleneck of data processing from sensors to communication systems. For example, analyzing IoT data often demands data centralization before running a mining algorithm. Thus, in order to reduce the data transference commonly required by the data clustering task, this paper proposes a grid-based data summarization approach. The proposed approach uses a single uniform grid to partition the space into cells and to summarize data before centralization. Summarization ensures the reduction of the amounts of data transferred. This approach also includes a data clustering algorithm that deals with the summarized and centralized data. Our preliminary experiments revealed good results in terms of data compression and quality of clustering with a two-dimensional benchmark dataset.

关键词： data summarization clusterization internet of things distributed data mining

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Accurate and large-scale privacy-preserving data mining using the election paradigm

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data & KNOWLEDGE ENGINEERING 2009年第11期68卷 1224-1236页

作者： Magkos, Emmanouil Maragoudakis, Manolis Chrissikopoulos, Vassilis Gritzalis, Stefanos Ionian Univ Dept Informat Corfu 49100 Greece Univ Aegean Dept Informat & Commun Syst Engn Karlovassi Samos Greece

With the proliferation of the Web and ICT technologies there have been concerns about the handling and use of sensitive information by data mining systems. Recent research has focused on distributed environments where the participants in the system may also be mutually mistrustful. In this paper we discuss the design and security requirements for large-scale privacy-preserving data mining (PPDM) systems in a fully distributed setting, where each client possesses its own records of private data. To this end we argue in favor of using some well-known cryptographic primitives, borrowed from the literature on Internet elections. More specifically, our framework is based on the classical homomorphic election model, and particularly on an extension for supporting multi-candidate elections. We also review a recent scheme [Z. Yang, S. Zhong, R.N. Wright, Privacy-preserving classification of customer data without loss of accuracy, in: SDM' 2005 SIAM International Conference on data mining, 2005] which was the first scheme that used the homomorphic encryption primitive for PPDM in the fully distributed setting. Finally, we show how our approach can be used as a building block to obtain Random Forests classification with enhanced prediction performance. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Security and privacy distributed data mining Homomorphic encryption Random Forests classification

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