Distributed Data Mining Based on Semantic Web and Grid

Wang, Huimin; Nie, Guihua; Fu, Kui

doi:10.1109/cinc.2009.230

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…In 2009, Huimin Wang, Guihua Nie and Kui Fu [21] proposed that Knowledge acquisition from distributed data resources to support decision-making is receiving an increasing attention. The paper proposes distributed knowledge acquisition architecture and puts forward any new solve algorithms for knowledge acquisition from large-scale distributed and heterogeneous data resources.…”

Section: Evolutionmentioning

confidence: 99%

A Novel Wireless Heterogeneous Data Mining (WHDM) Environment Based on Mobile Computing Environments

Pandey

Gupta

Dubey³

2011

2011 International Conference on Communication Systems and Network Technologies

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Today the concept of Data Mining services is not alone sufficient. Data mining services play an important role in the field of Communication industry. Data mining is also called knowledge discovery in several database including mobile databases and for heterogeneous environment. In this paper, we propose a novel algorithm named Wireless Heterogeneous Data Mining (WHDM). The entire system architecture consists of three phases: 1) Reading the Database. 2) Stores the value in Tbuf with different patterns. 3) Add the superset in the list and remove the related subset from the list. Finally we find the frequent pattern patterns or knowledge from huge amount of data.We also analyze the better method or rule of data mining services which is more suitable for mobile devices. Our simulation result shows better performance and efficiency on different heterogeneous environment.

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Section: Evolutionmentioning

confidence: 99%

A Novel Wireless Heterogeneous Data Mining (WHDM) Environment Based on Mobile Computing Environments

Pandey

Gupta

Dubey³

2011

2011 International Conference on Communication Systems and Network Technologies

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“…High dimensional spare data significantly deteriorate the reliability of the models derived from the data [10] common approaches are to employ dimension reduction or future selection [11] to reduce data dimension or to carefully include additional samples to alleviate the data scarcity, such as generic unsupervised learning methods in data mining.…”

Section: A High Dimensional Spare Datamentioning

confidence: 99%

Data Mining Task Tools Techniques and Applications

Gheware¹,

Kejkar²,

Tondare³

2014

International Journal of Advanced Research in Computer and Comm

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This paper deals with detail study of Data Mining its techniques, tasks and related Tools. Data Mining refers to the mining or discovery of new information in terms of interesting patterns, the combination or rules from vast amount of data. It helps in classifying, segmenting data and in hypothesis formation. With such a vast amount of data, there is need for powerful technique for better interpretation of these data. Including commercial and open source, many program available to perform data mining. Data mining tools predict future trends and behaviours, allowing business to make proactive and present knowledge in the form which is easily understood to human.

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Parallel and Distributed Spatial Outlier Mining in Grid: Algorithm, Design and Application

Chen

Lin

et al. 2015

J Grid Computing

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Distributed Data Mining Based on Semantic Web and Grid

Cited by 7 publications

References 11 publications

A Novel Wireless Heterogeneous Data Mining (WHDM) Environment Based on Mobile Computing Environments

A Novel Wireless Heterogeneous Data Mining (WHDM) Environment Based on Mobile Computing Environments

Data Mining Task Tools Techniques and Applications

Parallel and Distributed Spatial Outlier Mining in Grid: Algorithm, Design and Application

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