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检索条件"主题词=Distributed Data mining"

共 234 条记录，以下是31-40 订阅

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Research on the Personalized Privacy Preserving distributed data mining

Research on the Personalized Privacy Preserving Distributed ...

引用

2nd International Conference on Future Information Technology and Management Engineering

作者： Shen, Yanguang Shao, Hui Li, Yan Hebei Univ Engn Sch Informat Sci & Elect Engn Handan Peoples R China

ISBN: (纸本)9780769538808

In this paper we studied privacy preserving distributed data *** existing methods focus on a universal approach that exerts preservation in the same degree for all persons, without catering for their concrete needs. In view of this we innovatively proposed a new framework combining the Secure Multiparty Computation(SMC) with K-anonymity technology, and achieved personalized privacy preserving distributed data mining based on decision tree classification algorithm. Compared with other algorithms our methord could make a good trade-off point between privacy and accuracy, with high efficiency and low-overhead of computing and communication.

关键词： personalized privacy preserving distributed data mining decision tree classification SMC K-anonymity

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A Game Theory based Repeated Rational Secret Sharing Scheme for Privacy Preserving distributed data mining

A Game Theory based Repeated Rational Secret Sharing Scheme ...

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10th International Conference on Security and Cryptography (SECRYPT)

作者： Nanavati, Nirali R. Jinwala, Devesh C. Sardar Vallabhbhai Natl Inst Technol Surat India

ISBN: (纸本)9789897581311

Collaborative data mining has become very useful today with the immense increase in the amount of data collected and the increase in competition. This in turn increases the need to preserve the participants' privacy. There have been a number of approaches proposed that use Secret Sharing for privacy preservation for Secure Multiparty Computation (SMC) in different setups and applications. The different multiparty scenarios may have parties that are semi- honest, rational or malicious. A number of approaches have been proposed for semi honest parties in this setup. The problem however is that in reality we have to deal with parties that act in their self- interest and are rational. These rational parties may try and attain maximum gain without disrupting the protocol. Also these parties if cautioned would correct themselves to have maximum individual gain in the future. Thus we propose a new practical game theoretic approach with three novel punishment policies with the primary advantage that it avoids the use of expensive techniques like homomorphic encryption. Our proposed approach is applicable to the secret sharing scheme among rational parties in distributed data mining. We have analysed theoretically the proposed novel punishment policies for this approach. We have also empirically evaluated and implemented our scheme using Java. We compare the punishment policies proposed in terms of the number of rounds required to attain the Nash equilibrium with eventually no bad rational nodes with different percentage of initial bad nodes.

关键词： Privacy Game Theory Secure Multiparty Computation Rational Secret Sharing distributed data mining

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An Agent-based Architecture for Supply Chain Finance Cooperative Context-aware distributed data mining Systems

An Agent-based Architecture for Supply Chain Finance Coopera...

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3rd International Conference on Internet and Web Applications and Services (ICIW 2008)

作者： Liu Xiang Shanghai Univ Sch Business & Management Shanghai 200444 Peoples R China

ISBN: (纸本)9781424442386

The unlimited growth of supply chain finance data will inevitably lead to a situation in which it is increasingly difficult to access the desired information. Supply chain finance is often necessary to analyze large data sets, maintained over geographically bank, supplier and customer distributed sites by using cooperative context-aware distributed data mining (DDM) systems. The study of the existing approaches shows that no single solution fulfills all requirements identified for the cooperative context-aware DDM systems. One of the basic obstacles is the lack of context-aware and supporting some of the computational resources - such as data and information bases, computational models, compute power to execute these models, specialized data mining algorithms - required to develop a new compound is not available locally, but accessible via the global computing network infrastructure. This paper proposes a multi-agent-based architecture for supply chain finance cooperative distributed data mining systems. The use of multi-agent-systems (MAS) creates a framework which allows the inter-operation of a vast set of heterogeneous solutions to carry out the complex supply chain finance context-aware distributed data mining tasks, many data mining tasks to connect heterogeneous resources, as data sources, processing nodes and end user applications. It considers a data warehousing that supports context-aware OLAP queries, ensuring the interoperability of all data sources, and then focuses on distributed clustering algorithms and some potential applications in multi-agent-based problem solving scenarios. Finally, we outline the implementation of a prototype for shoes manufacturing

关键词： Cooperative management multi-agent-systems Shoes manufacturing Context-Aware distributed data mining Supply Chain Finance

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International Workshop on Federated Learning for distributed data mining 23

International Workshop on Federated Learning for Distributed...

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29th ACM SIGKDD Conference on Knowledge Discovery and data mining (KDD)

作者： Hong, Junyuan Zhu, Zhuangdi Lyu, Lingjuan Zhou, Yang Boddeti, Vishnu Naresh Zhou, Jiayu Michigan State Univ E Lansing MI 48824 USA Microsoft Seattle WA USA Sony AI Tokyo Japan Auburn Univ Auburn AL USA

ISBN: (纸本)9798400701030

The past decade has witnessed wide applications of machine learning to various domains for decision-making, including crime detection, urban planning, drug discovery, and health monitoring, which benefited from surging data resources. As data collection in real-world applications is often done in different locations, being able to mine and discover knowledge from distributed data sources is an essential requirement for building powerful predictive models. However, directly uploading all data sources to an untrustworthy centralized data server for learning will lead to risks of privacy leakage. Federated Learning (FL) emerges as a decentralized learning framework that aggregates knowledge from distributed data without centralizing them, hence mitigating privacy risks. By hosting this workshop, we aim to attract a broad spectrum of audiences, including researchers and practitioners from academia and industry interested in the latest advances in FL. As an effort to advance the fundamental development of FL in data mining, this workshop will encourage ideas exchange on the trustworthiness, scalability, robustness, and broad applications of FL.

关键词： federated learning distributed data mining trustworthiness applications

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Analytical Study of Agent Based distributed data mining and its Ontology

Analytical Study of Agent Based Distributed Data Mining and ...

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8th International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Pathak, Bimalendu Sinha, Madhavi Birla Inst Technol Dept Comp Sci Jaipur Rajasthan India Birla Inst Technol Dept Comp Sci Jaipur Rajasthan India

ISBN: (纸本)9789380544120

With the increased complexity in number of applications and due to large volume of availability of data from heterogeneous sources, there is a need fir the development of suitable ontology, which can handle the large data set and present the mined outcomes for evaluation intelligently. In the era of intensive data driven applications distributed data mining can meet the challenges with the support of agents. This paper discusses the underlying principles for effectiveness of modern agent-based systems for distributed data mining.

关键词： Intelligent agents Ontology Multi-Agent System distributed data mining

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A distributed data mining Framework Accelerated with Graphics Processing Units

A Distributed Data Mining Framework Accelerated with Graphic...

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International Conference on Cloud Computing and Big data (CLOUDCOM-ASIA)

作者： Nam-Luc Tran Dugauthier, Quentin Skhiri, Sabri Euranova R&D Mont St Guibert Belgium

ISBN: (纸本)9781479928293

In the context of processing high volumes of data, the recent developments have led to numerous models and frameworks of distributed processing running on clusters of commodity hardware. On the other side, the Graphics Processing Unit (GPU) has seen much enthusiastic development as a device for general-purpose intensive parallel computation. In this paper we propose a framework which combines both approaches and evaluates the relevance of having nodes in a distributed processing cluster that make use of GPU units for further fine-grained parallel processing. We have engineered parallel and distributed versions of two data mining problems, the naive Bayes classifier and the k-means clustering algorithm, to run on the framework and have evaluated the performance gain. Finally, we also discuss the requirements and perspectives of integrating GPUs in a distributed processing cluster, introducing a fully distributed heterogeneous computing cluster.

关键词： GPU algorithm data mining distributed data mining distributed processing kmeans naive bayes processing distributed processing data mining Graphics Processing Unit GRAPPER PICK UP Frameworks Bayesian classifier

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A Framework for Cost-Effective distributed data mining in academic institutions using Intelligent Agents 4

A Framework for Cost-Effective Distributed Data Mining in ac...

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4th International Conference on HCT Information Technology Trends (ITT)

作者： Raman, Ramakrishnan Vadivel, S. Raj, Benson Edwin Higher Coll Technol Dubai Womens Coll CIS Dept Dubai U Arab Emirates BITS Pilani CS Dept Dubai Campus Pilani Rajasthan India Higher Coll Technol Fujairah Womens Coll CIS Dept Dubai U Arab Emirates

ISBN: (纸本)9781538633304

The purpose of this paper is to illustrate the maximum utilization of available computing resources for the data mining activities in the academic institutions. data mining playing a huge role in predicting future from various data bases which includes weather databases, financial data portals. Emerging disease information systems has been recognized by industries as an important area. This brings the opportunity of major revenues from applications such as business data warehousing, process control, and personalized on-line customer services over Internet and web. distributed data mining (DDM) is expected to perform partial analysis of data at the client's place and then send the outcome as results to the server where it is sometimes required to be aggregated to the global result. The primary issues to be considered for DDM are accuracy, scalability, privacy and autonomy of data. These issues can be easily handled with the intelligent software agents for DDM, because of its autonomous, adaptive and deliberative reasoning features. Apart from this, the positive side of this approach is that the complete system can be built using open-source environment such as Java Runtime Environment (JRE) which is a cost-effective approach and use existing computing resources available in the institution.

关键词： distributed data mining Intelligent agents JRE Apriori algorithm Resource utilization

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A game theoretic approach to active distributed data mining

A game theoretic approach to active distributed data mining

引用

IEEE/WIC/ACM International Conference on Intelligent Agent Technology and Web Intelligence

作者： Zhang, Xiaofeng Cheung, William K. Hong Kong Baptist Univ Dept Comp Sci Kowloon Hong Kong Peoples R China

ISBN: (纸本)9780769530277

Learning-from-abstraction(LFA) is a recently proposed model-based distributed data mining approach which aims to the mining process both scalable and privacy preserving. However how to set the right trade-off between the abstraction levels of the local data sources and the global model accuracy is crucial for getting the optimal abstraction, especially when the local data are inter-correlated to different extents. In this paper we define the optimal abstraction task as a game and compute the Nash equilibrium as its solution. Also, we propose an iterative version of the game so that the Nash equilibrium can be computed by actively exploring details from the local sources in a need-to-know manner. We tested the proposed game theoretic approach using a number of data sets for model-based clustering with promising results obtained.

关键词： distributed data mining privacy preservation game theory active learning

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Collective Dendrogram Clustering with Collaborative Filtering for distributed data mining on Electronic Health Records 2

Collective Dendrogram Clustering with Collaborative Filterin...

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2nd IEEE International Conference on Electrical, Computer and Communication Technologies (IEEE ICECCT)

作者： Urmela, S. Nandhini, M. Pondicherry Univ Dept Comp Sci Pondicherry India

ISBN: (纸本)9781509032396

distributed data mining (DDM) on Electronic Health Records (EHRs) has evolved in large space aiming for effective and efficient record retrieval with minimized communication cost and memory cost than Centralized data mining(CDM). In this paper, heterogeneous classifier techniques of DDM are compared and their performance is evaluated with respect to EHR as dataset. Finally an architectural model based on Collective Dendrogram Clustering with Collaborative Filtering (CDC-CF) on Electronic Health Records is proposed with its components.

关键词： distributed data mining Electronic Health Records heterogeneous classifier approach Dendrogram clustering Collaborative Filtering

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A Customizable Multi-Agent System for distributed data mining 07

A Customizable Multi-Agent System for Distributed Data Minin...

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22nd ACM Symposium on Applied Computing

作者： Di Fatta, Giuseppe Fortino, Giancarlo Univ Reading Sch Syst Engn Reading RG6 6AY Berks England Univ Calabria DEIS I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9781595934802

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.

关键词： Multi-Agent Systems distributed data mining Agent Oriented Software Engineering Dynamic Load Balancing Peer-To-Peer Computing

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