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Discrete distribution clustering in ...

Zhang, Yu.

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Discrete distribution clustering in big data and a method for storm prediction leveraging large historical archives.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Discrete distribution clustering in big data and a method for storm prediction leveraging large historical archives./
Author:	Zhang, Yu.
Description:	97 p.
Notes:	Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
Contained By:	Dissertation Abstracts International76-12B(E).
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3715583
ISBN:	9781321938173

Discrete distribution clustering in big data and a method for storm prediction leveraging large historical archives.
Zhang, Yu.

Discrete distribution clustering in big data and a method for storm prediction leveraging large historical archives. - 97 p.

Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2015.

Big data brings new challenges and opportunities in many scientific areas today. Characterized by the high volume, velocity, and variety (3Vs) model, big data is valuable in many knowledge discovery applications, whereas requires new methodologies and technologies to manage and make use of the data. In this dissertation, a fundamental methodology and an emerging application of big data are presented. First, the parallel discrete distribution (PD2) clustering algorithm is designed and implemented. Discrete distributions are well adopted data signatures in information retrieval and machine learning, and discrete distribution (D2) clustering is a fundamental methodology. However, the high computational complexity of D2-clustering limits its impact on massive learning problems. PD2-clustering with substantially improved scalability facilitates unsupervised learning in many big data applications. Extensive analysis and experiments are presented to demonstrate the effectiveness and advantages of PD2-clustering. Second, satellite image analysis for storm forecasting is explored as an application of big data in meteorology. A large amount of historical satellite images and storm report archives are mined to predict storms. The proposed algorithm extracts visual storm signatures from satellite image sequences in a way similar to how meteorologists interpret them, and incorporates past meteorological records to model and classify the signatures. Such a big-data-driven approach aims at overcoming the intrinsic numerical instability of the conventional weather forecasting approach based on physical numerical models, and serves as a new component in a weather forecasting system. Experimental results in both studies show the benefits of leveraging big data in multiple areas.

ISBN: 9781321938173Subjects--Topical Terms:

523869
Computer science.

Discrete distribution clustering in big data and a method for storm prediction leveraging large historical archives.
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245 1 0 $a Discrete distribution clustering in big data and a method for storm prediction leveraging large historical archives.
300 $a 97 p.
500 $a Source: Dissertation Abstracts International, Volume: 76-12(E), Section: B.
500 $a Advisers: James Z. Wang; Jia Li.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2015.
520 $a Big data brings new challenges and opportunities in many scientific areas today. Characterized by the high volume, velocity, and variety (3Vs) model, big data is valuable in many knowledge discovery applications, whereas requires new methodologies and technologies to manage and make use of the data. In this dissertation, a fundamental methodology and an emerging application of big data are presented. First, the parallel discrete distribution (PD2) clustering algorithm is designed and implemented. Discrete distributions are well adopted data signatures in information retrieval and machine learning, and discrete distribution (D2) clustering is a fundamental methodology. However, the high computational complexity of D2-clustering limits its impact on massive learning problems. PD2-clustering with substantially improved scalability facilitates unsupervised learning in many big data applications. Extensive analysis and experiments are presented to demonstrate the effectiveness and advantages of PD2-clustering. Second, satellite image analysis for storm forecasting is explored as an application of big data in meteorology. A large amount of historical satellite images and storm report archives are mined to predict storms. The proposed algorithm extracts visual storm signatures from satellite image sequences in a way similar to how meteorologists interpret them, and incorporates past meteorological records to model and classify the signatures. Such a big-data-driven approach aims at overcoming the intrinsic numerical instability of the conventional weather forecasting approach based on physical numerical models, and serves as a new component in a weather forecasting system. Experimental results in both studies show the benefits of leveraging big data in multiple areas.
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