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Peer sampling gossip-based distributed clustering algorithm for unstructured P2P networks

NEURAL COMPUTING & APPLICATIONS 2018年第2期29卷 593-612页

作者： Azimi, Rasool Sajedi, Hedieh Islamic Azad Univ Qazvin Branch Young Researchers & Elite Club Qazvin Iran Univ Tehran Coll Sci Sch Math Stat & Comp Sci Dept Comp Sci Tehran Iran

Clustering, as an unsupervised learning method and an important process in data mining, is an aspect of large and distributed data analysis. In many applications, such as peer-to-peer systems, huge volumes of data are distributed between multiple sources. Analysis of these volumes of data and identifying appropriate clusters is challenging due to transmission, processing and storage costs. In this paper, a gossip-based distributed clustering algorithm for P2P networks called Efficient GBDC-P2P is proposed, based on an improved gossip communicative approach by combining the peer sampeling and CYCLON protocol and the idea of partitioning-based data clustering. This algorithm is appropriate for data clustering in unstructured P2P networks, and it is adapted to the dynamic conditions of these networks. In the Efficient GBDC-P2P algorithm, distributed peers perform clustering operation in a distributed way only through local communications with their neighbors. Our approach does not rely on the central server to carry out data clustering task and without the need to synchronize operations. Evaluation results verify the efficiency of our proposed algorithm for data clustering in unstructured P2P networks. Furthermore, comparative analyses with other well-established distributed clustering approaches demonstrate the superior accuracy of the proposed method.

关键词： distributed data mining Clustering Gossiping Overlay Peer-to-peer network

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distributed classification for image spam detection

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第11期77卷 13249-13278页

作者： Amir, Amiza Srinivasan, Bala Khan, Asad I. Univ Malaysia Perlis Sch Comp & Commun Engn Arau Perlis Malaysia Monash Univ Fac Informat Technol Melbourne Vic Australia

Spam appears in various forms and the current trend in spamming is moving towards multimedia spam objects. Image spam is a new type of spam attacks which attempts to bypass the spam filters that mostly text-based. Spamming attacks the users in many ways and these are usually countered by having a server to filter the spammers. This paper provides a fully-distributed pattern recognition system within P2P networks using the distributed associative memory tree (DASMET) algorithm to detect spam which is cost-efficient and not prone to a single point of failure, unlike the server-based systems. This algorithm is scalable for large and frequently updated data sets, and specifically designed for data sets that consist of similar occurring *** have evaluated our system against centralised state-of-the-art algorithms (NN, k-NN, naive Bayes, BPNN and RBFN) and distributed P2P-based algorithms (Ivote-DPV, ensemble k-NN, ensemble naive Bayes, and P2P-GN). The experimental results show that our method is highly accurate with a 98 to 99% accuracy rate, and incurs a small number of messages-in the best-case, it requires only two messages per recall test. In summary, our experimental results show that the DAS-MET performs best with a relatively small amount of resources for the spam detection compared to other distributed methods.

关键词： P2P classification distributed pattern recognition Spam detection Image spam distributed classification distributed data mining P2P data mining

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A high-performance distributed algorithm for mining association rules

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KNOWLEDGE AND INFORMATION SYSTEMS 2005年第4期7卷 458-475页

作者： Schuster, A Wolff, R Trock, D Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel Technion Israel Inst Technol Dept Elect Engn IL-32000 Haifa Israel

We present a new distributed association rule mining (D-ARM) algorithm that demonstrates superlinear speed-up with the number of computing nodes. The algorithm is the first D-ARM algorithm to perform a single scan over the database. As such, its performance is unmatched by any previous algorithm. Scale-up experiments over standard synthetic benchmarks demonstrate stable run time regardless of the number of computers. Theoretical analysis reveals a tighter bound on error probability than the one shown in the corresponding sequential algorithm. As a result of this tighter bound and by utilizing the combined memory of several computers, the algorithm generates far fewer candidates than comparable sequential algorithms-the same order of magnitude as the optimum.

关键词： association rule data mining distributed data mining high-performance computing

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An agent-based framework for distributed learning

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2011年第1期24卷 93-102页

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

In this paper an agent-based distributed learning framework based on data reduction is proposed. data reduction aims at finding patterns or regularities within certain features, allowing to induce the so-called prototypes which should be retained for further use during the learning process. The considered approach assumes that data reduction through instance and feature selection is carried out independently at each site by a team of agents. To assure obtaining homogenous prototypes the feature selection requires coordination. The proposed approach provides such coordination by collaboration of agents. In the process of data reduction heterogeneous prototypes can be subsequently merged to create a compact representation of the distributed data repositories and, next, based on such a compact representation a selected meta-learning technique can be applied for generating the global classifier. The paper proposes and explains strategies for agent collaboration producing a common set of features and strategies for constructing combiner classifier. Suggested strategies are evaluated experimentally and compared. The paper includes a detailed description of the proposed approaches and a discussion of the computational experiment results. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： distributed data mining distributed learning Multi-agent system data reduction

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A gossip based information fusion protocol for distributed frequent itemset mining

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ENTERPRISE INFORMATION SYSTEMS 2018年第6期12卷 674-694页

作者： Sohrabi, Mohammad Karim Islamic Azad Univ Semnan Branch Dept Comp Engn Semnan Iran

The computational complexity, huge memory space requirement, and time-consuming nature of frequent pattern mining process are the most important motivations for distribution and parallelization of this mining process. On the other hand, the emergence of distributed computational and operational environments, which causes the production and maintenance of data on different distributed data sources, makes the parallelization and distribution of the knowledge discovery process inevitable. In this paper, a gossip based distributed itemset mining (GDIM) algorithm is proposed to extract frequent itemsets, which are special types of frequent patterns, in a wireless sensor network environment. In this algorithm, local frequent itemsets of each sensor are extracted using a bit-wise horizontal approach (LHPM) from the nodes which are clustered using a leach-based protocol. Heads of clusters exploit a gossip based protocol in order to communicate each other to find the patterns which their global support is equal to or more than the specified support threshold. Experimental results show that the proposed algorithm outperforms the best existing gossip based algorithm in term of execution time.

关键词： distributed data mining frequent itemset mining Gossip based protocol bit wise approach wireless sensor network

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distributed probabilistic inferencing in sensor networks using variational approximation

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JOURNAL OF PARALLEL AND distributed COMPUTING 2008年第1期68卷 78-92页

作者： Mukherjee, Sourav Kargupta, Hillol Univ Maryland Dept Comp Sci & Elect Engn Baltimore MD 21201 USA

This paper considers the problem of distributed inferencing in a sensor network. It particularly explores the probabilistic inferencing problem in the context of a distributed Boltzmann machine-based framework for monitoring the network. The paper offers a variational mean-field approach to develop communication-efficient local algorithm for variational inferencing in distributed environments (VIDE). It compares the performance of the proposed approximate variational technique with respect to the exact and centralized techniques. It shows that the VIDE offers a much more communication-efficient solution at very little cost in terms of the accuracy. It also offers experimental results in order to substantiate the scalability of the proposed algorithm. (c) 2007 Elsevier Inc. All rights reserved.

关键词： probabilistic inferencing variational approximation distributed data mining sensor network data mining

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distributed identification of top-l inner product elements and its application in a peer-to-peer network

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2008年第4期20卷 475-488页

作者： Das, Kamalika Bhaduri, Kanishka Liu, Kun Kargupta, Hillol Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA IBM Corp Almaden Res Ctr San Jose CA 95120 USA AGNIK LLC Columbia MD 21045 USA

The inner product measures how closely two feature vectors are related. It is an important primitive for many popular data mining tasks, for example, clustering, classification, correlation computation, and decision tree construction. If the entire data set is available at a single site, then computing the inner product matrix and identifying the top (in terms of magnitude) entries is trivial, However, in many real-world scenarios, data is distributed across many locations and transmitting the data to a central server would be quite communication intensive and not scalable. This paper presents an approximate local algorithm for identifying top-l inner products among pairs of feature vectors in a large asynchronous distributed environment such as a peer-to-peer (P2P) network. We develop a probabilistic algorithm for this purpose using order statistics and the Hoeffding bound. We present experimental results to show the effectiveness and scalability of the algorithm. Finally, we demonstrate an application of this technique for interest-based community formation in a P2P environment.

关键词： distributed data mining inner product peer-to-peer network

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Performance evaluation of a WSN system for distributed event detection using fuzzy logic

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AD HOC NETWORKS 2014年 23卷 87-108页

作者： Dima, Sofia Maria Panagiotou, Christos Tsitsipis, Dimitris Antonopoulos, Christos Gialelis, John Koubias, Stavros Univ Patras Dept Elect & Comp Engn Patras Greece

The research field of event detection in realistic WSN environments has attracted a lot of interest, with health monitoring being one of its most pronounced applications. Although efforts related to the healthcare applications exist in the current literature, there is a significant lack of investigation on the performance of such systems, when applied in error prone and limited resource wireless environments. This paper aimed to address this need by porting a Fuzzy Inference System (FIS) to a WSN simulation framework. The considered FIS is implemented on TelosB motes and evaluates the health status of a monitored person, in an energy conserving manner. A distributed implementation of the above FIS is also proposed, comprising an additional contribution of this paper, based on an objective function, attempting to reduce the network congestion and balance the energy consumption between network nodes. This work presents a thorough performance evaluation of the FIS under the distributed and the centralized approach, while varying the communication conditions and highlighting the advantages of the distributed execution of the FIS, leading to packet loss gain and transmission gain up to 67% and 25% respectively. The networking benefits from the distributed approach are reflected to the FIS performance. Respective results and comparative evaluation against Matlab simulations reveal strong dependencies of the application's performance to critical WSN network parameters. (C) 2014 Elsevier B.V. All rights reserved.

关键词： WSNs Event recognition Fuzzy logic Health status Task scheduling distributed data mining

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Prototype selection algorithms for distributed learning

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PATTERN RECOGNITION 2010年第6期43卷 2292-2300页

作者： Czarnowski, Ireneusz Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

distributed learning from data is one of the typical tasks solved by distributed data-mining techniques and is seen as a fundamental computational problem. One of the approaches suitable for distributed learning is to select, by data reduction, relevant local patterns, called also prototypes, from geographically distributed databases. Next, locally selected prototypes can be moved to other sites and merged into the global knowledge model. The paper presents three agent-based population learning algorithms for distributed learning. The proposed algorithms are based on agent collaborations in distributed prototype selection processes and on agent collaborations when the learning global model is created. The basic property of the presented algorithms is that the prototypes are selected by agent-based population learning algorithm from data clusters induced at distributed sites. The main goal of the paper is to empirically compare how the way of inducing such clusters can influence the distributed learning performance. The paper investigates the agent-based population learning algorithms used to solve distributed data reduction and gives a brief discussion of the procedures for clusters initialization. Finally, computational experiment results are shown. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： distributed data mining distributed learning data reduction Instance selection

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distributed volunteer computing for solving ensemble learning problems

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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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