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A method for extracting rules from spatial data based on rough fuzzy sets

Hexiang Bai, Yong Ge, Jinfeng Wang, Deyu Li, Yilan Liao, Xiaoying Zheng
2014 Knowledge-Based Systems  
This paper presents a model based on rough fuzzy sets to extract spatial fuzzy decision rules from spatial data that simultaneously have two types of uncertainties, roughness and fuzziness.  ...  Spatial rule extraction from spatial data with uncertainty is an important issue in spatial data mining. Rough set theory is an effective tool for rule extraction from data with roughness.  ...  ., roughness and fuzziness, needed to be handled in this case. Two extensions of rough set theory provide tools for handling data of this type.  ... 
doi:10.1016/j.knosys.2013.12.008 fatcat:s2xkcjrj35cjtfm5wkww7tla6m


Sharmila Banu Kather, BK Tripathy
2016 Jurnal Teknologi  
Data Science has been evolving and when analyzed with Soft Computing techniques like Rough Set Theory (RST), Fuzzy Sets, Granulation Computing which encompasses the data in its original nature, results  ...  This survey paper highlights Spatial Data Mining methods used in the field of Epidemiology, identifies crucial challenges and discusses of the use of Soft Computing methods.  ...  Spatial Auto Co-Relation Indexes Moran's index is a global index used to measure how an attribute under analysis is correlated with a geographic location. It takes a value between -1 and +1.  ... 
doi:10.11113/jt.v78.7879 fatcat:g4hdnijd4bdqfbwqdssxgl3gsy

Using rough set theory to identify villages affected by birth defects: the example of Heshun, Shanxi, China

Hexiang Bai, Yong Ge, Jin-Feng Wang, Yi Lan Liao
2010 International Journal of Geographical Information Science  
This article uses rough set theory to explore spatial decision rules in neural-tube birth defects and searches for novel spatial factors related to the disease.  ...  Moreover, a novel relationship between the spatial attributes and the neural-tube birth defects was discovered.  ...  In the field of spatial data analysis, some researchers have argued for the advantages of using rough sets and have also provided in recent works some methodologies for handling spatial data.  ... 
doi:10.1080/13658810902960079 fatcat:kngmtggnczhyve44fkxxavf3qy

Research on Rough Set and Decision Tree Method Application in Evaluation of Soil Fertility Level [chapter]

Guifen Chen, Li Ma
2011 IFIP Advances in Information and Communication Technology  
Treatment strategy of missing values for some attribute is assigned to its most common value corresponding training examples, another more complex strategy is to give a probability for each possible value  ...  Johnson rough set attribute reduction algorithm can reduce attributes to the frequency by property size and weight, access a collection of the most relevant, can calculate the reduction effectively.  ...  In view of rough set and decision tree has a strong complementary nature, this paper introduces rough set theory and decision tree in data mining field, uses original survey 3 Applications Based on Rough  ... 
doi:10.1007/978-3-642-18336-2_50 fatcat:7nze5daacna23fnbpfcp5ixose

Integrating rough set theory and medical applications

Puntip Pattaraintakorn, Nick Cercone
2008 Applied Mathematics Letters  
We present a short survey of ongoing research and a case study on integrating rough set theory and medical application.  ...  Issues in the current state of rough sets in advancing medical technology and some of its challenges are also highlighted.  ...  If a concept is 'not definable' in a given knowledge base, rough sets can 'approximate' with respect to that knowledge. From a medical point of view, the attribute-value boundaries are usually vague.  ... 
doi:10.1016/j.aml.2007.05.010 fatcat:njqx5ng5rfgilhtaszsktqdwnq


H. Sheikhian, M. R. Delavar, A. Stein
2015 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this paper, a new methodology to handle uncertainty for multi-criteria decision making problems is proposed.  ...  The criteria were discretized and the data set was granulated using a hierarchical rough method, where the best describing granules are determined according to the quality measures.  ...  Hierarchical rough granulation The granules defined by rough set approximation can have a nested sequence, in which an equivalence granule thus forming a multi-layered granulation structure.  ... 
doi:10.5194/isprsannals-ii-3-w5-271-2015 fatcat:x7lcl6gimvgafemaf5debtxvgi

Fuzzy - Rough Feature Selection With Π- Membership Function For Mammogram Classification [article]

K.Thangavel, R.Roselin
2012 arXiv   pre-print
In this paper, Fuzzy-Rough feature selection with π membership function is proposed. The selected features are used to classify the abnormalities with help of Ant-Miner and Weka tools.  ...  Oncologists are miserably failed in identifying the micro calcification at the early stage with the help of the mammogram visually.  ...  It starts off with an empty set and adds in turn, one at a time, those attributes that result in the greatest increase in the rough set dependency metric, until this produces its maximum possible value  ... 
arXiv:1205.4336v2 fatcat:hxkda5ha7zeuloh3lg7sdxpfda

Multispectral remote sensing image classification algorithm based on rough set theory

Ying Wang, Xiaoyun Liu, Zhensong Wang, Wufan Chen
2009 2009 IEEE International Conference on Systems, Man and Cybernetics  
First, to decrease computational time and complexity, band reduction of multispectral image using attribute reduct concept in rough set theory and information entropy is performed.  ...  Rough set theory is a relatively new mathematical tool to deal with imprecise, incomplete and inconsistent data. A method of multispectral image classification using rough set theory is proposed.  ...  Description of Attributes Discretization Based on Rough Set Theory Decision system ( , , , ) S U A F d , l V is the value domain of attribute l a .  ... 
doi:10.1109/icsmc.2009.5346054 dblp:conf/smc/WangLWC09 fatcat:2b3chwcgifhddjzkkpovyn2jmi

Outlier Detection in Neutrosophic Sets by using Rough EntropyBased Weighted Density Method

Sangeetha T, Geetha Mary A
2020 CAAI Transactions on Intelligence Technology  
Fuzzy sets provide a solution for uncertainties, and intuitionistic fuzzy sets handle incomplete information, but both concepts failed to handle indeterminate information.  ...  The weighted density outlier detection method based on rough entropy calculates weights of each object and attribute.  ...  There is a chance of abnormal occurrences in spatial or temporal locality forms a cluster known as anomalies or outliers.  ... 
doi:10.1049/trit.2019.0093 fatcat:amuwaydftrb5rmdyh74kuv2tb4


H. Kiavarz, M. Jadidi, A. Rajabifard, G. Sohn
2018 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
a spatial database and 3D BIM data.  ...  This paper focuses on providing a rules extraction and supervised-decision making methods for facilitating the fusion of BIM and 2D and 3D GIS-based information coupling with IoT stream data residing in  ...  Intuitively, a set of attributes DF depends totally on a set of conditional attributes P ∈ CF, if all attribute values from DF are uniquely arranged by the values of attributes from P.  ... 
doi:10.5194/isprs-archives-xlii-4-w10-79-2018 fatcat:apg2nftmvvhx3h6o4yjlkumq2m

Multispectral image segmentation using the rough-set-initialized EM algorithm

S.K. Pal, P. Mitra
2002 IEEE Transactions on Geoscience and Remote Sensing  
Rough-set theory helps in faster convergence and in avoiding the local minima problem, thereby enhancing the performance of EM.  ...  For rough-set-theoretic rule generation, each band is discretized using fuzzy-correlation-based gray-level thresholding. MST enables determination of nonconvex clusters.  ...  Let there be sets of discretized objects in the attribute-value table having identical attribute values, and let their cardinalities be , . Let denote the distinct elements among such that .  ... 
doi:10.1109/tgrs.2002.803716 fatcat:tkji6ssauzg6bletzicv4g6mni

A formal approach to imperfection in geographic information

M. Duckham, K. Mason, J. Stell, M. Worboys
2001 Computers, Environment and Urban Systems  
Error, or inaccuracy, concerns a lack of correlation of an observation with reality; imprecision concerns a lack of specificity in representation.  ...  Based on an ontology of imperfection the paper explores a formal model of imperfect geographic information using multi-valued logic.  ...  ACKNOWLEDGEMENTS This project is supported by the EPSRC under grant GR/M 56685 "Managing vagueness, uncertainty and granularity in spatial information systems", funded jointly with the School of Computer  ... 
doi:10.1016/s0198-9715(00)00040-5 fatcat:ol3iya74ivechj3gvypcnvgpzy

An information-fusion method to identify pattern of spatial heterogeneity for improving the accuracy of estimation

Lianfa Li, Jinfeng Wang, Zhidong Cao, Ershun Zhong
2007 Stochastic environmental research and risk assessment (Print)  
Data mining is major analysis components in our method: multivariate statistics, association analysis, decision tree and rough set are used in data filter, identification of contributing factors, and examination  ...  While spatial autocorrelation is used in spatial sampling survey to improve the precision of the feature's estimate of a certain population at area units, spatial heterogeneity as the stratification frame  ...  Acknowledgments This research has been done in support of the grants 40601077/D0120 and 40471111/D0120 from the Natural Science Foundation of China, and the grant 2007AA12Z233 from Hi-tech Research and  ... 
doi:10.1007/s00477-007-0179-1 fatcat:qidqux46rvabjb46dyhhpgqmn4

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Yetao Yang, Yi Wang, Ke Wu, Xin Yu
2016 Remote Sensing  
A rough set-based imagery classifier is a rule-induction system that comprises a set of features (attributes) and the related decision rules for classification.  ...  To improve the recognition and handle the uncertainty, this paper provides a novel integrated approach, based on a fuzzy rough set and evidential reasoning (FRSER), for land cover classification in an  ...  Fuzzy rough theory, which is an extension of classical rough set theory, provides a solid foundation for handling a fuzzified rough set [33, 34] .  ... 
doi:10.3390/rs8040304 fatcat:6bkq7c3mw5defi4otkhh6igyum

Techniques for Machine Learning based Spatial Data Analysis: Research Directions

M. Gangappa, C. Kiran, P. Sammulal
2017 International Journal of Computer Applications  
Today, machine learning techniques play a significant role in data analysis, predictive modeling and visualization.  ...  This paper reviews state-of-the-art in the domain of spatial data analysis by employing machine learning approaches. First various methods have been summarized, which exist in the literature.  ...  set of value that attribute a may take.  ... 
doi:10.5120/ijca2017914643 fatcat:6uhdrkatzfd5vnd7cx7my7bnpa
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