The Analysis on Dimensionality Reduction Mathematical Model Based on Feedback Constraint for High-Dimensional Information

Peng Wu

doi:10.4028/www.scientific.net/AMR.846-847.1056

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 846-847The Analysis on Dimensionality Reduction...

The Analysis on Dimensionality Reduction Mathematical Model Based on Feedback Constraint for High-Dimensional Information

Abstract:

This paper proposes a dimensionality reduction mathematical model based on feedback constraint for High-dimensional information. It uses feedback restriction technique to construct dimensionality reduction model for multidimensional product data. The data obtained is with high latitudes, where a large number of data are under components involved standardized restrictions. High-dimensional data participating in operation will increase the complexity of operation, and hence, we need to reduce its dimension. In this paper multi-constrained inverse regression model is adopted to reduce the dimension of cloud resource scheduling data in multi-constrained environments. Experimental results show that the proposed method increases the data coverage rate of high-dimensional data mining by 66%, and has great optimizing effect.

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Periodical:

Advanced Materials Research (Volumes 846-847)

Pages:

1056-1059

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.846-847.1056

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Online since:

November 2013

Authors:

Peng Wu*

Keywords:

Data Mining (DM), Dimensionality Reduction, High-Dimensional Data

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References

[1] R Cooley, J Srivastava. Grouping web page references into transactions for mining world wide web browsing patterns[C]. Proceedings of KDEX'97, NewportBeach, CA, USA, 1997, 2-7.

DOI: 10.1109/kdex.1997.629824

Google Scholar

[2] R. Agrawal, T. Imielinski and A. Swami. Mining association rules between sets of items in large database[C], The 1993 ACM-SIGMOD, Washington, USA, 1993: 207~16.

DOI: 10.1145/170036.170072

Google Scholar

[3] H. Mannila, H. Toivonen and A. I. Verkamo. Efficient algorithms for discovering association rules[C]. In KDD-94: AAAI Workshop on Knowledge Discovery in Database, Seattle, Washington, 1994: 181~192.

Google Scholar

[4] H. Toivonen, Sampling Large Database for Association Rules. 22nd VLDB Conference, 1996: 134-145. I. Breiman, J. Friedman, and R. Olshen. Classification of Regression Trees. Wadsworth, (1998).

Google Scholar

[5] A. Sarasere, E. Omiecinsky and S. Navathe. An efficient algorithm for mining association rules in large Databases. The 21st Int'l Conf on Very large Database, Zurich, Switzerland, (1995).

Google Scholar