Spatial Data Mining: Progress and Challenges Survey Paper

Knowledge Discovery in Spatial Databases Progress and Challenges

Spatial data, i.e., data related to objects that occupy space, are continuosly being collected for various applications ranging from remote sensing, geographical information systems (GIS) to computer cartography and environmental assesment and planing. The volume of data collected is so huge that it has become humanely impossible to do any intelligent data analysis. Even though very few methods...

Detection of Breast Cancer Progress Using Adaptive Nero Fuzzy Inference System and Data Mining Techniques

Prediction, diagnosis, recovery and recurrence of the breast cancer among the patients are always one of the most important challenges for explorers and scientists. Nowadays by using of the bioinformatics sciences, these challenges can be eliminated by using of the previous information of patients records. In this paper has been used adaptive nero fuzzy inference system and data mining techniqu...

A Study on Application of Spatial Data Mining Techniques for Rural Progress

This paper focuses on the application of Spatial Data mining Techniques to efficiently manage the challenges faced by peripheral rural areas in analyzing and predicting market scenario and better manage their economy. Spatial data mining is the task of unfolding the implicit knowledge hidden in the spatial databases. The spatial Databases contain both spatial and non-spatial attributes of the a...

A Recent Survey on Knowledge Discovery in Spatial Data Mining

Spatial data mining is the process of discovering, motivating and previously unknown, but potentially helpful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more tricky than extracting the parallel patterns from established numeric and definite data due to the complexity of spatial data types, spatial relationships, and spatial autocorr...

Spatial Data Mining: Progress and Challenges