Queries for Uncertain Data on Dataspace Based on Effective Clustering Algorithm

Shuang Zhang; Shi Xiong Zhang

doi:10.4028/www.scientific.net/AMM.380-384.1529

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 380-384Queries for Uncertain Data on Dataspace Based on...

Queries for Uncertain Data on Dataspace Based on Effective Clustering Algorithm

Abstract:

This paper presents a probabilistic data stream clustering method P-Stream. An effective clustering algorithm called P-Stream for probabilistic data stream is developed in this paper for the first time. For the uncertain tuples in the data stream, the concepts of strong cluster, transitional clusters and weak cluster are proposed in the P-Stream. With these concepts, an effective strategy of choosing candidate cluster is designed, which can find the sound cluster for every continuously arriving data point. In this paper, we systematically defined the dataspace, the uncertain data, and proposed a updated algorithm of queries on uncertain data based on Effective Clustering Algorithm.

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Periodical:

Applied Mechanics and Materials (Volumes 380-384)

Pages:

1529-1532

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.380-384.1529

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Online since:

August 2013

Authors:

Shuang Zhang, Shi Xiong Zhang

Keywords:

Data Model, Dataspace, Query Processing, Uncertain Data

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