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检索条件"主题词=distributed data mining"

共 234 条记录，以下是181-190 订阅

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Privacy Preserving distributed Classification: A Satisfaction Equilibrium Approach

Privacy Preserving Distributed Classification: A Satisfactio...

引用

IEEE Global Telecommunications Conference (GLOBECOM)

作者： Xu, Lei Jiang, Chunxiao Li, Jianhua Zhao, Youjian Ren, Yong Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Tsinghua Space Ctr Beijing 100084 Peoples R China Shanghai Jiao Tong Univ Coll Informat Secur Shanghai 201203 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

ISBN: (纸本)9781509050192

The privacy issue arising in data mining applications has attracted much attention in recent years. In the context of distributed data mining, the participant can employ data perturbation techniques to protect its privacy. data perturbation generally causes a negative effect on the mining result, which means there is a trade-off between privacy and the mining result. In this paper, we study a distributed classification scenario where a number of users provide data to a mediator to train a classifier. Interactions among users are modeled as a game in satisfaction form. And an algorithm is proposed for users to learn the satisfaction equilibrium (SE) of the game. The basis idea is that the user gradually reduces the perturbation in data until it is satisfied with the classification accuracy. Experimental results based on real data demonstrate that, when the differences among users' expectations are not significant, the proposed learning algorithm can converge to an SE, at which every user achieves a balance between the classification accuracy and the preserved privacy.

关键词： distributed data mining privacy preserving game theory satisfaction equilibrium equilibrium learning

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distributed Opportunistic Sensing in Mobile Phone Sensor Networks

Distributed Opportunistic Sensing in Mobile Phone Sensor Net...

引用

IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

作者： Viet-Duc Le Univ Twente Pervas Syst Grp NL-7500 AE Enschede Netherlands

ISBN: (纸本)9781467350778;9781467350754

The advantages of smartphones such as integrated sensors, programmability, scalability and cloud servers have enabled low-cost and efficient public safety applications. However, designing such applications has to face daunting challenges, for instance, short battery life, low computing capability and lacking memory. To this end, lighter, faster, more efficient and scalable distributed data processing algorithms are necessary. In particular, new mechanisms are necessary for adaptive and opportunistic sensing within a cluster of smartphones. Furthermore, a distributed data mining algorithm is in need of proposition to run on each smartphone for information retrieval. The retrieved information also needs a new algorithm to be fused and even enhanced by more powerful devices such as sensor nodes in the infrastructure to obtain higher accuracy and reliability. For experimental validation, the new algorithms will be implemented on smartphones to collect and detect abnormalities from measured environmental elements such as sounds, pressure, temperature, light, etc., under various public safety scenarios including ambient noise. Currently, I am working from the scratch on a new idea that using a few preferable sensors to trigger others' measurement for adaptive and opportunistic sensing. This saves considerable battery power and lessens interference with normal phone usage.

关键词： addaptive sensing opportunistic sensing distributed data mining anomaly detection smartphones

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Hybrid data mining Algorithm in Cloud Computing using MapReduce Framework

Hybrid Data Mining Algorithm in Cloud Computing using MapRed...

引用

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

作者： Sahay, Siddharth Khetarpal, Suruchi Pradhan, Tribikram Manipal Univ Manipal Inst Technol Dept Informat & Commun Technol Manipal Karnataka India

ISBN: (纸本)9781467395458

' data mining' has transformed into a ubiquitous term in the world of IT and Computer Science in recent times. Developments in this field have been countless. Using one of Apriori algorithm's numerous variants with a couple of insightful additions can significantly improve upon the existing standard of data mining. In this paper a new approach to considerably reduce the time complexity of the database scan has been proposed. This has been achieved by using the MapReduce framework for Hadoop distributed File System (HDFS). Coupled with Cloud computing, which handles large data sets and processing remotely, the resultant system - that uses MapReduce for the full table scan, the Pincer-Search Algorithm, and Cloud Computing - is a force to reckon with.

关键词： Cloud computing MapReduce distributed data mining Maximum Frequent Item Sets Pincer-Search Algorithm

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DAC-SGD: A distributed Stochastic Gradient Descent Algorithm Based on Asynchronous Connection 17

DAC-SGD: A Distributed Stochastic Gradient Descent Algorithm...

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2nd International Conference on Intelligent Information Processing (IIP)

作者： He, Aijia Chen, Zehong Li, Weichen Li, Xingying Li, Hongjun Zhao, Xin Beijing Normal Univ Sch Management 18 Jinfeng Rd Zhuhai City Guangdong Peoples R China

ISBN: (纸本)9781450352871

In the data mining practice, it happens that the algorithm used in mining tasks needs to deal with the multiple distributed data source, while the required datasets are located in different companies or organizations and reside in different system and technology environments. In traditional mining solutions or algorithms, data located in different source need to be copied and integrated into a homogenous computation environment, and then the mining can be executed, which leads to large data transmission and high storage costs. Even the data mining can be infeasible due to the data ownership problems. In this paper, a distributed asynchronous connection approach for the well-used stochastic gradient descent algorithm (SGD) was presented, and a distributed implementation for it was done to cope with the multiple distributed data source problems. In which, the main process of the algorithm was executed asynchronously in distributed computation node and the model can be trained locally in multiple data sources based on their own computation environment, so as to avoid the data integration and centralized processing. And the feasibility and performance for the proposed algorithm was evaluated based on experimental studies.

关键词： distributed data mining Machine Learning Stochastic Gradient Descent Multiple distributed data Source Asynchronous Connection

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data mining at the IoT Edge 28

Data Mining at the IoT Edge

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28th International Conference on Computer Communications and Networks (ICCCN)

作者： Savaglio, Claudio Gerace, Pietro Di Fatta, Giuseppe Fortino, Giancarlo DIMES Arcavacata Di Rende CS Italy Univ Reading Dept Comp Sci Reading Berks England

ISBN: (纸本)9781728118567

The Internet of Things (IoT) enables the interconnection of new cyber-physical devices which generate significant traffic of distributed, heterogeneous and dynamic data at the network edge. Since several IoT applications demand for short response times (e.g., industrial applications, emergency management, real-time systems) and, at the same time, rely on resource-constrained devices, the adoption of traditional data mining techniques is neither effective nor efficient. Therefore, conventional data mining techniques need to be adjusted for optimizing response times, energy consumption and data traffic while still providing adequate accuracy as required by the IoT application. In this paper, new data mining approaches particularly tailored for the IoT scenario have been investigated, in particular with respect to the promising, emerging novel distributed computing paradigm of Edge Computing. In detail, two approximated versions of K-Means clustering algorithm, centralized and distributed, have been implemented in the EdgeCloudSim simulation framework and validated on a real system. As highlighted by the algorithm performance analysis, choosing an approximated and distributed clustering solution can provide benefits in terms of computation, communication and energy consumption, while maintaining high levels of accuracy. The management of such trade-off, obviously, has to be done in the light of the specific IoT application requirements.

关键词： Edge Computing distributed data mining Internet of Things Clustering

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Rough Set Approach for distributed Decision Tree and Attribute Reduction in the Disseminated Environment

Rough Set Approach for Distributed Decision Tree and Attribu...

引用

International Conference on Advances in Information Technology and Mobile Communication (AIM 2011)

作者： Chandra, E. Ajitha, P. DJ Acad Managerial Excellence Dept Comp Sci Coimbatore Tamil Nadu India

ISBN: (纸本)9783642205729

Attribute reduction is a necessitated step for the disseminated environment in regard to classification and prediction of the data. Traditional approaches were not efficient for optimal attribute reducts. Current techniques are quiet time consuming and less accuracy. Rough set approach is a mathematical technique to handle attribute reducts through data dependencies and structural methods. This paper discusses a novel algorithm for optimal attribute deduct and also increases the accuracy in predicted results and the distributed decision tree classification techniques was made use of to implement the same in the disseminated environment. Proposed algorithm for Construction of distributed decision trees with rough sets increases the accuracy and also reduces the attributes on the time of the massive data sets handling.

关键词： Rough Sets distributed Decision Trees distributed data mining

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Experiments in Multi Agent Learning

Experiments in Multi Agent Learning

引用

3rd International Workshop on Hybrid Artificial Intelligence Systems

作者： Gaya, Maria Cruz Giraldez, J. Ignacio Univ Europea Madrid Madrid 28690 Spain

ISBN: (纸本)9783540876557

data sources are often dispersed geographically in real life applications. Finding a knowledge model may require to join all the data sources and to run a machine learning algorithm on the joint set. We present an alternative based on a Multi Agent System (MAS): an agent mines one data source in order to extract a local theory (knowledge model) and then merges it with the previous MAS theory using a knowledge fusion technique. This way, we obtain a global theory that summarizes the distributed knowledge without spending resources and time in joining data sources. The results show that, as a result of knowledge fusion, the accuracy of initial theories is improved as well as the accuracy of the monolithic solution.

关键词： distributed data mining ensemble techniques MAS evolutionary computation

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An Efficient Approach for Privacy Preserving distributed mining of Association Rules in Unsecured Environment

An Efficient Approach for Privacy Preserving Distributed Min...

引用

International Conference on Advances in Computing, Communications and Informatics ICACCI

作者： Modi, Chirag N. Patil, Ashwini R. Doshi, Nishant Natl Inst Technol Goa Ponda Goa India Natl Inst Technol Surat Surat Gujarat India MEFGI Dept Comp Engn Rajkot 360003 Gujarat India

ISBN: (纸本)9781479987924

distributed data mining techniques are widely used for many applications viz;marketing, decision making, statistical analysis etc. In distributed data environment, each of the involving sites contains local information which will be collaborated to extract global mining result. However, these techniques have been investigated in terms of privacy and security concerns of individual site's information. To solve this problem, many cryptography techniques have been investigated. Still there is a room for further improvement. In this paper, we propose an efficient approach for privacy preserving distributed association rule mining. We use onion routing protocol in order to exchange information among involving sites. We use an elliptic curve (EC) based cryptography in order to achieve security and privacy of individual site's information in unsecured distributed environment. Finally, we analyze proposed solution in terms of security, privacy, computational cost and communication cost.

关键词： distributed data mining Association Rules Onion Routing Elliptic Curve Cryptography

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Performance Evaluation of distributed Association Rule mining Algorithms 7

Performance Evaluation of Distributed Association Rule Minin...

引用

7th International Conference on Communication, Computing and Virtualization (ICCCV)

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

Association Rule mining (ARM) is a popular and well researched method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. Most ARM algorithms focus on a sequential or centralized environment where no external communication is required. distributed ARM algorithms (DARM), aim to generate rules from different data sets spread over various geographical sites;hence, they require external communications throughout the entire process. DARM algorithm efficiency is highly dependent on data distribution. The Classical algorithms used in DARM are Count Distribution Algorithm (CDA), Fast distributed mining (FDM) Algorithm and Optimized distributed Association mining (ODAM) Algorithm. This paper presents the implementation details and experimental results of above mentioned algorithms. The paper also highlights the issues of message exchange size in a distributed environment of current DARM algorithms that can affect the communication costs in a distributed environment. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Association Rule mining distributed data mining

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Scalability, Memory Issues and Challenges in mining Large data Sets 6

Scalability, Memory Issues and Challenges in Mining Large Da...

引用

2014 International Conference on Intelligent Networking and Collaborative Systems (IEEE INCoS 2014)

作者： Kolici, Vladi Xhafa, Fatos Barolli, Leonard Lala, Algenti Polytech Univ Tirana Tirana Albania Univ Politecn Cataluna Barcelona Spain Fukuoka Inst Technol Fukuoka Japan

ISBN: (纸本)9781479963874

data mining is an active field of research and development aiming to automatically extract "knowledge" from analyzing data sets. Knowledge can be defined in different ways such as discovering (structured, frequent, approximate, etc.) patterns in data, grouping/clustering/bi-clustering data according to one or more criteria, finding association rules, etc. Such knowledge is then fed-back to decision support systems enabling end-users (actors) to make more informed decisions, which in economic terms could lead to advantages as compared to traditional decision support systems. It should be noted however, that data mining algorithms and frameworks have been proposed prior to the "Big data" explosion. While data mining algorithms have considered efficiency and computational complexity as an important requirement, they did not take into account features of Big data such as very large size, velocity with which data is generated, variety, etc. On the other hand, these features are indeed posing issues and challenges to data mining algorithms and frameworks. In this paper we analyse some of the issues in mining large data sets such as scalability and in-memory needs. We also show some computational results pointing out to such issues.

关键词： data mining Large data Sets distributed data mining Hadoop Map Reduce Scalability Memory

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