A data mining approach for prediction of cultivated land demand in land use planning

Yaolin Liu; Zuohua Miao; Wenfei Chen

doi:10.1117/12.651854

2 December 2005 A data mining approach for prediction of cultivated land demand in land use planning

Yaolin Liu, Zuohua Miao, Wenfei Chen

Proceedings Volume 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications; 604537 (2005) https://doi.org/10.1117/12.651854
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

Although data mining is relative young technique, it has been used in a wide range of problem domains over the past few decades. In this paper, the authors present a new model to forecast the cultivated land demand adopts the technique of data mining. The new model which is called fuzzy Markov Chain model with weights ameliorate the traditional Time Homogeneous Finite Markov chain model to predict the future value of cultivated land demand in land use planning. The new model applied data mining technique to extract useful information from enormous historical data and then applied fuzzy sequential cluster method to set up the dissimilitude fuzzy clustering sections. The new model regards the standardized self-correlative coefficients as weights based on the special characteristics of correlation among the historical stochastic variables. The transition probabilities matrix of new model was obtained by using fuzzy logic theory and statistical analysis method. The experimental results shown that the ameliorative model combined with technique of data mining is more scientific and practical than traditional predictive models.

Citation Download Citation

Yaolin Liu, Zuohua Miao, and Wenfei Chen "A data mining approach for prediction of cultivated land demand in land use planning", Proc. SPIE 6045, MIPPR 2005: Geospatial Information, Data Mining, and Applications, 604537 (2 December 2005); https://doi.org/10.1117/12.651854

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available