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检索条件"主题词=distributed data mining"

共 234 条记录，以下是151-160 订阅

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HP2PC: Scalable Hierarchically-distributed Peer-to-Peer Clustering

HP2PC: Scalable Hierarchically-Distributed Peer-to-Peer Clus...

引用

7th SIAM International Conference on data mining

作者： Hammouda, Khaled M. Karnel, Mohamed S. Univ Waterloo Syst Design Engn PAMI Grp Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9780898716306

In distributed data mining models, adopting a flat node distribution model can affect scalability. To address the problem of modularity, flexibility and scalability, we propose a hierarchically-distributed peer-to-peer architecture and algorithm for data clustering (HP2PC). The architecture is based on a multi-layer overlay network of peer neighborhoods. Supernodes, which act as representatives of neighborhoods, are recursively grouped to form higher level neighborhoods. Peers at a certain level of the hierarchy cooperate within their respective neighborhoods to perform clustering. Using this model, we can partition the clustering problem in a modular way, solve each part individually, then successively combine clusterings up the hierarchy where increasingly global solutions are computed. The algorithm was applied to a distributed document clustering problem and achieved decent speedup with comparable clustering quality to the centralized approach.

关键词： distributed data mining peer-to-peer clustering hierarchically-distributed clustering

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A Multi-Perspective distributed mining Framework for Scalable Search Spell Correction 33

A Multi-Perspective Distributed Mining Framework for Scalabl...

引用

IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Li, Yutong Bommaganti, Hari Yadava, Himanshu Apple Inc Cupertino CA 95014 USA

ISBN: (纸本)9781665408981

Acquiring high-quality misspelling data at large scale to train quality search speller models is key but challenging. Synthetic data generation approaches as a major focus in literature are usually linguistics dependent, challenging in domain adaptation, and centered around empirical choices based on error patterns generalized from limited annotated datasets. mining based approaches on the other hand are not sufficiently studied and don't ensure ground-truth corrections. Both methodologies also lack focus on other strategical considerations which matter for the final quality of the data and model. We introduce a novel, comprehensive and production-proved distributed mining framework which is able to generate large-scale quality data to train search speller models. The enabling method eliminates dependency on human judged data, and fully scales exploring, training and deploying high-quality speller models with maximal efficiency. The work has been demonstrated by production launches of spell correction to worldwide markets for Apple Maps search. Our approach should also facilitate the general synthetic data generation approaches in applicable domains to get rid of the human annotation dependency.

关键词： scalable machine learning spell correction distributed data mining auto-complete search

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Efficient distributed Approach for Density-Based Clustering

Efficient Distributed Approach for Density-Based Clustering

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20th IEEE International Workshops on Enabling Technologies - Infrastructure for Collaborative Enterprises (WETICE)

作者： Laloux, Jean-Francois Le-Khac, Nhien-An Kechadi, M-Tahar Univ Mons Fac Polytech Mons B-7000 Mons Belgium Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9780769544106

Nowadays, large bodies of data in different domains are collected and stored. An efficient extraction of useful knowledge from these data becomes a huge challenge. This leads to the need for developing distributed data mining techniques. However, only a few research concerns distributed clustering for analysing large, heterogeneous and distributed datasets. Besides, current distributed clustering approaches are normally generating global models by aggregating local results that would lose important knowledge. In this paper, we present a new distributed data mining approach where local models are not directly merged to build the global ones. Preliminary results of this algorithm are also discussed. (Abstract)

关键词： distributed data mining clustering balance vector large datasets distributed platform

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A new approach for distributed density based clustering on grid platform

A new approach for distributed density based clustering on g...

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24th British National Conference on databases (BNCOD 24)

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

ISBN: (纸本)9783540733898

Many distributed data mining DDM tasks such as distributed association rules and distributed classification have been proposed and developed in the last few years. However, only a few research concerns distributed clustering for analysing large, heterogeneous and distributed datasets. This is especially true with distributed density-based clustering although the centralised versions of the technique have been widely used fin different real-world applications. In this paper, we present a new approach for distributed density-based clustering. Our approach is based on two main concepts: the extension of local models created by DBSCAN at each node of the system and the aggregation of these local models by using tree based topologies to construct global models. The preliminary evaluation shows that our approach is efficient and flexible and it is appropriate with high density datasets and a moderate difference in dataset distributions among the sites.

关键词： distributed data mining distributed clustering density-based large dataset tree topology

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data mining for the Internet of Things with Fog Nodes 16th

Data Mining for the Internet of Things with Fog Nodes

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16th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems (NEW2AN) / 9th Conference on Internet of Things and Smart Spaces Are You Smart (ruSMART)

作者： Kholod, Ivan Petuhov, Ilya Efimova, Maria St Petersburg Electrotech Univ LETI St Petersburg Russia

ISBN: (纸本)9783319463018;9783319463001

The paper describes an approach of applying an actor model that executes data mining algorithms to analyze data in IoT systems with a distributed architecture (with Fog Computing). The approach allows to move computational load closer to the data, thus increasing performance of the analysis and decreasing network traffic. Execution of the 1R algorithm in an IoT system with a distributed architecture and the results of the comparison of distributed and centralized architectures are shown in the paper.

关键词： Internet of Things Fog Computing data mining distributed data mining Actor model

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distributed BitTable Multi-Agent Association Rules mining Algorithm

Distributed BitTable Multi-Agent Association Rules Mining Al...

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15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES)

作者： Atteya, Walid Adly Dahal, Keshav Hossain, M. Alamgir Univ Bradford Sch Comp Informat & Media Bradford BD7 1DP W Yorkshire England Northumbria Univ Sch Comp Engn & Informat Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9783642238505;9783642238512

Many algorithms have been proposed for the discovery of association rules. The efficiency of these algorithms needs to be improved to handle real-world large datasets. This efficiency can be determined mainly by three factors. The way candidates are generated, the way their supports are counted and the data structure used. Most papers focus on the first and the second factors while few focus on the underlying data structures. In this paper, we present a distributed Multi-Agent based algorithm for mining association rules in distributed environments. The distributed MAS algorithm uses Bit vector data structure that was proved to have better performance in centralized environments. The algorithm is implemented in the context of Multi-Agent systems and complies with global communication standard Foundation for Intelligent Physical Agents (FIPA). The distributed Multi-Agent based algorithm with its new data structure improves implementations reported in the literature that were based on Apriori. The algorithm has better performance over Apriori-like algorithms.

关键词： Multi-Agent Systems distributed data mining Association Rules

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Privacy Preserving Associative Classification on Vertically Partitioned databases

Privacy Preserving Associative Classification on Vertically ...

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IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)

作者： Raghuram, B. Gyani, Jayadev Kakatiya Inst Sci & Technol Dept Comp Sci & Engg Warangal Andhra Pradesh India Jayamukhi Inst Technol Sci Dept Comp Sci & Engn Warangal Andhra Pradesh India

ISBN: (纸本)9781467320467

The growing needs of multiple parties interaction in corporate and financial sector emphasize the need of developing privacy preserving and efficient distributed data mining algorithms. Even though a lot of research work is progressing in this area to transform efficient centralized mining models to work on horizontal and vertical partitioned databases there is lack of associative classification model that can perform classification on vertically partitioned databases. In order to overcome such needs this paper proposes an associative classification model on vertically partitioned databases. By considering privacy requirements in case of data sharing among multiple parties a scalar product based third party privacy preserving model adopted for proposed model. The proposed model accuracy tested on VCI data bases given encouraging results.

关键词： Associative classification Vertically partitioned data base distributed data mining Privacy preservig

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Research on Privacy Preserving distributed C4. 5 Algorithm

Research on Privacy Preserving Distributed C4. 5 Algorithm

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3rd International Symposium on Intelligent Information Technology Application

作者： Shen, Yanguang Shao, Hui Huang, Jianzhong Hebei Univ Engn Sch Informat Sci & Elect Engn Handan Hebei Peoples R China Handan Xingxing Heavy Machinery Co Ltd Handan Hebei Peoples R China

ISBN: (纸本)9781424464203

This paper studied how two parties collaboratively built a decision tree on the union of their dataset without revealing privacy when dataset is vertically and horizontally distributed. We gave an algorithm of privacy preserving C4.5 which is applicable to vertically and horizontally partitioned dataset, and also gave the detailed computation method of the information gain ratio in the case of without revealing privacy. The secure scalar product protocol, the xln(x) protocol and the secure sum protocol are used in collaborative computing, which can protect privacy effectively.

关键词： privacy preserving distributed data mining C4.5 decision tree secure multiparty calculation

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Efficient Discovery of data mining Services over DHT-based Overlays

Efficient Discovery of Data Mining Services over DHT-based O...

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International Conference on High Performance Computing & Simulation (HPCS)

作者： Folino, Gianluigi Pisani, Francesco Trunfio, Paolo ICAR CNR Arcavacata Di Rende CS Italy Univ Calabria DIMES I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9781479953134

Service-oriented architectures are widely adopted to implement distributed data analysis applications that exploit decentralized data mining services. Standard Web service (WS) technologies are often used to provide access to such services, in order to facilitate their composition and to effectively handle heterogeneity of data mining algorithms and implementations. In this paper we propose a framework for efficiently discovering data mining services in large-scale networks, taking into account the specific features of the algorithms and implementations of such services. The proposed framework, named WS-Chord, includes a taxonomy of data mining services used to annotate the WSs, and an extension of the Chord DHT protocol to efficiently index and discover annotated WSs. A locality preserving hashing (LPH) function, which takes into account the taxonomy hierarchy, has been defined to support efficient service discovery over WS-Chord when queries are formulated against the proposed taxonomy. Experimental results have demonstrated the effectiveness of the framework in terms of relevant data mining services discovered.

关键词： distributed data mining Web services distributed Hash Tables Chord WS-Chord

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An Incremental mining with ODAM (IODAM)

An Incremental Mining with ODAM (IODAM)

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International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)

作者： Sawant, Vinaya Shah, Ketan Mumbai Univ DJ Sanghvi Coll Engn IT Dept Bombay Maharashtra India NMIMS MPSTME IT Dept Bombay Maharashtra India

ISBN: (纸本)9781509061068

Association rules are used to discover interesting patterns of discrete data located in transactional database or another information repository. In many organizations, data is not stored in centralized location but dispersed at various geographical locations. The aim of distributed Association Rule mining (DARM) is to determine interesting patterns from the datasets that are spread over various geographical sites. DARM focuses on minimizing communication costs between datasets at different locations. Optimized distributed Association mining (ODAM) is an existing algorithm which addresses DARM requirement. Since the use of record-based database is growing and data is continuously being added, there is a need to develop a solution that will generate rules for the new set of transactions without re-scanning the previously mined data. The paper suggests an improvement in ODAM by implementing incremental mining approach so that the ODAM algorithm can work efficiently on continuous transactional database.

关键词： distributed Association Rule mining distributed data mining Association Rules Incremental mining

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