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检索条件"主题词=distributed data mining"

共 234 条记录，以下是21-30 订阅

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Deploying mobile agents in distributed data mining

Deploying mobile agents in distributed data mining

引用

11th Pacific-Asia Conference on Knowledge Discovery and data mining

作者： Li, Xining Ni, Jinglo Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China Univ Guelph Dept Comp & Informat Sci Guelph ON Canada

ISBN: (纸本)9783540770169

mining information from distributed data sources over the Internet is a growing research area. The introduction of mobile agent paradigm opens a new door for distributed data mining and knowledge discovery applications. In this paper, we present the design of a mobile agent system, which couples a logic language based application programming interface for service discovery and database access. Our proposal aims at implementing system tools to enable intelligent mobile agents to search for distributed data services, to roams the Internet for accessing distributed data sites, to discover patterns and extract useful information from facts recorded in databases, to generate global data model through the communication and aggregation of local results, and to overcome the barriers posed by network congestion, poor security and unreliability.

关键词： mobile agent distributed data mining service discovery database service

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HealthSCOPE: An Interactive distributed data mining Framework for Scalable Prediction of Healthcare Costs 14

HealthSCOPE: An Interactive Distributed Data Mining Framewor...

引用

14th IEEE International Conference on data mining (IEEE ICDM)

作者： Marquardt, James Newman, Stacey Hattarki, Deepa Srinivasan, Rajagopalan Sushmita, Shanu Ram, Prabhu Prasad, Viren Hazel, David Ramesh, Archana De Cock, Martine Teredesai, Ankur Univ Washington Tacoma Ctr Data Sci Inst Technol 1900 Commerce St Tacoma WA 98402 USA Edifecs 2600 116th Ave Ne 200 Bellevue WA 98004 USA Univ Ghent Dept Appl Math Comp Sci & Stat Krijgslaan 281 S9 B-9000 Ghent Belgium

ISBN: (纸本)9781479942749

In this demonstration proposal we describe HealthSCOPE (Healthcare Scalable COst Prediction Engine), a framework for exploring historical and present day healthcare costs as well as for predicting future costs. HealthSCOPE can be used by individuals to estimate their healthcare costs in the coming year. In addition, HealthSCOPE supports a population based view for actuaries and insurers who want to estimate the future costs of a population based on historical claims data, a typical scenario for accountable care organizations (ACOs). Using our interactive data mining framework, users can view claims (sample files will be provided), use HealthSCOPE to predict costs for the upcoming year, interactively select from a set of possible medical conditions, understand the factors that contribute to the cost, and compare costs against historical averages. The back-end system contains cloud based prediction services hosted on the Microsoft Azure infrastructure that allow the easy deployment of models encoded in Predictive Model Markup Language (PMML) and trained using either Spark MLLib or various non-distributed environments.

关键词： Healthcare cost prediction insurance claims data distributed data mining Spark Microsoft Azure PMML

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Optimization and Scheduling Algorithm for data Intensive Workflows in distributed data mining Architecture 17

Optimization and Scheduling Algorithm for Data Intensive Wor...

引用

IEEE EUROCON -17th International Conference on Smart Technologies

作者： Kakasevski, Gorgi Mishev, Anastas FON Univ Fac Informat Skopje North Macedonia Ss Cyril & Methodius Univ Fac Comp Sci & Engn Skopje North Macedonia

ISBN: (纸本)9781509038435

New Grid and cloud solutions for distributed data mining and data processing are needed for execution of data intensive workflows. In contrast of the standard workflows, in which data between the jobs are exchanged in the form of files and the jobs are finished when they process the input data, data intensive workflows receive data organized in blocks which are streamed on inputs, analyze the data and produce stream output. Each job is active for a long period of time and can receive new data. In our previous research works we proposed the Open Grid Service Architecture for data mining (OGSA-DM), which is capable of executing data intensive workflows. According to our analysis, the current algorithms for scheduling workflows can't be applied on data intensive workflows because they produce unsatisfactory results and can't guarantee optimal solution. In this paper we propose new optimization and scheduling algorithm which is developed on the advantages of data intensive workflows. In several experiments we've shown that our proposed algorithm works and gives satisfactory results.

关键词： Grid computing distributed data mining scheduling algorithm

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DXCS: an XCS system for distributed data mining 05

DXCS: an XCS system for distributed data mining

引用

Genetic and Evolutionary Computation Conference

作者： Dam, Hai H. Abbass, Hussein A. Lokan, Chris UNSW ADFA Sch ITEE Robot Lab Canberra ACT 2600 Australia

ISBN: (纸本)1595930108

XCS is a flexible system for data mining due to its ability to deal with environmental changes, learn online with little prior knowledge and evolve accurate and maximally general classifiers. In this paper, we propose DXCS which is an XCS-based distributed data mining system. A MDL metric is proposed to quantify and analyze network load, and study the balance between network load and classifier accuracy in the presence of noise. The DXCS system shows promising results.

关键词： algorithms performance measurement learning classifier system XCS MDL distributed data mining

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PERFORMANCE PREDICTION OF STUDENTS USING distributed data mining 2

PERFORMANCE PREDICTION OF STUDENTS USING DISTRIBUTED DATA MI...

引用

2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS)

作者： Parmar, Krina Vaghela, Dineshkumar Sharma, Priyanka Parul Inst Technol Vadodara India Parul Inst Technol Dept CSE Vadodara India ISTAR MCA Dept Vallabh Vidhyanagar India

ISBN: (纸本)9781479968183

The performance of students in higher education in India is a turning point in the academics for all students for their brightest career. In today's generation the amount of data stored in educational database increasing at a great rate. These databases contain secret information for improvement of students' performance;these data can be located at different nodes in distributed system. Classification and prediction are among the major techniques in data mining and widely used in various fields. In this paper classification techniques are used for prediction of student performance in distributed environment. data mining methods are often implemented at many advance universities today for analyzing available data and extracting information and knowledge to support *** While it is important to have models at local level, their results makes it difficult to extract knowledge that can be useful at the global level. Therefore, to support decision making at this area, it is important to generalize the information contained in those models, specific classifier method can be used to generalize these rules for global model.

关键词： Classification Clustering Prediction distributed data mining Educational data mining

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Multi-agent systems and distributed data mining 8th

Multi-agent systems and distributed data mining

引用

8th International Workshop on Cooperative Information Agents

作者： Giannella, C Bhargava, R Kargupta, H Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

ISBN: (纸本)3540231706

Multi-agent systems offer an architecture for distributed problem solving. distributed data mining algorithms specialize on one class of such distributed problem solving tasks-analysis and modeling of distributed data. This paper offers a perspective on distributed data mining algorithms in the context of multiagents systems. It particularly focuses on distributed clustering algorithms and their potential applications in multi-agent-based problem solving scenarios. It discusses potential applications in the sensor network domain, reviews some of the existing techniques, and identifies future possibilities in combining multi-agent systems with the distributed data mining technology.

关键词： multi-agent systems distributed data mining clustering

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Research on distributed data mining in E-commerce Environment Based on Web Services and Mobile Agent

Research on Distributed Data Mining in E-commerce Environmen...

引用

International Conference on Computational Intelligence and Natural Computing

作者： Hou, Jinbiao Dezhou Univ Dept Comp Sci & Technol Dezhou Peoples R China

ISBN: (纸本)9780769536453

Modern e-commerce is vigorously developing. It makes the resourses and services on Internet more richly colorful. Every day these rich resourses and services can produce many new mass data which contain a large number of important information. These data are often heterogeneous, uncertain and unstructured. Their complexity has far exceeded the analysis and understanding ability which the humanity has at present. In order to resolve the problem, related technologies of distributed data mining in e-commerce environment are studied. Web services technology and agent technology are introduced in the e-commerce data mining. The distributed data mining structure in e-commerce environment leased on Web services and mobile agent is put forward.

关键词： e-commerce Web services mobile agent distributed data mining architecture

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Incentive-Compatible Privacy-preserving distributed data mining

Incentive-Compatible Privacy-preserving Distributed Data Min...

引用

IEEE 13th International Conference on data mining (ICDM)

作者： Kantarcioglu, Murat Univ Texas Dallas Dept Comp Sci Dallas TX 75230 USA

ISBN: (纸本)9780769551098

The quantity of data that is captured, collected, and stored by a wide variety of organizations is growing at an exponential rate. The potential for such data to support scientific discovery and optimization of existing systems is significant, but only if it can be integrated and analyzed in a meaningful way by a wide range of investigators. While many believe that data sharing is desirable, there are also privacy and security concerns, rooted in ethics and the law that often prevent many legitimate and noteworthy applications. In this talk, we will provide an overview on research regarding how to integrate and mine large amounts of privacy-sensitive distributed data without violating such constraints. Especially, we will discuss how to incentivize data sharing in privacy-preserving distributed data mining applications. This work will draw upon examples form the biomedical domain and discuss recent research on privacy-preserving mining of genomic databases.

关键词： distributed data mining incentives privacy Privacy data mining incentives data Sharing scientific discovery Distribute Speaking Ethics

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A Failure Handling Framework for distributed data mining Services on the Grid

A Failure Handling Framework for Distributed Data Mining Ser...

引用

19th International Euromicro Conference on Parallel, distributed and Network-Based Processing (PDP)

作者： Cesario, Eugenio Talia, Domenico ICAR CNR Arcavacata Di Rende Italy Univ Calabria ICAR CNR Arcavacata Di Rende Italy

ISBN: (纸本)9780769543284

Fault tolerance is an important issue in Grid computing, where many and heterogenous machines are used. In this paper we present a flexible failure handling framework which extends a service-oriented architecture for distributed data mining previously proposed, addressing the requirements for fault tolerance in the Grid. The framework allows users to achieve failure recovery whenever a crash can occur on a Grid node involved in the computation. The implemented framework has been evaluated on a real Grid setting to assess its effectiveness and performance.

关键词： distributed data mining Fault Tolerance Grid computing

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Cloud Computing & Multi-Agent Systems: A New Promising Approach for distributed data mining

Cloud Computing & Multi-Agent Systems: A New Promising Appro...

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34th International Conference on Information Technology Interfaces (ITI)

作者： Othmane, Benyoucef Hebri, Rahal Sidi Ahmed Univ Sci & Technol Oran Dept Informat Oran Algeria

ISBN: (纸本)9789537138240;9781467316293

The distributed data mining (DDM) become one of the major research topics in information systems over the few past years, after the growth of the networks connectivity, and the data volume as well. At the same time the cloud computing made its appearance and proving that it's the future of: the modern computerization and the new promising solution for resource distribution. The last two points fits the requirements of this new technology to be adapted for the resolution of the DDM problems. In this paper we will present our new approach for the distributed data mining with using of the technologies of the multi-agent systems and the cloud computing. We propose an agent based system that work under a cloud service (SaaS) architecture as a modular, extensible, easy to develop and use DDM system.

关键词： distributed data mining Cloud Computing Multi-Agent Systems

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