Spatial data mining focuses on searching rules of the geographical statement, the structures of distribution and the spatial patterns of phenomena. However, many methods ignore the temporal information, thus, limited results describing the statement of spatial phenomena. This paper focuses on developing a mining method which directly detects spatial-temporal association rules hidden in the geog...

This paper focusses on designing and applying data mining techniques to analyze spatial and spatiotemporal data originated in scientific domains. Data mining is the process of discovering hidden and meaningful knowledge in a data set. It has been successfully applied to many real-life problems, for instance, web personalization, network intrusion detection, and customized Marketing. This paper ...

fuzzy rule-based classification system (frbcs) is a popular machine learning technique for classification purposes. one of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. however many cases the minority classes are more important than the majority ones. in this paper, we have extended ...

Most rule induction algorithms including those for association rule mining use high support as one of the main measures of interestingness. In this paper we follow an opposite approach and describe an algorithm, called Optimist, which finds all largest empty intervals in data and then transforms then into the form of multiple-valued rules. It is demonstrated how this algorithm can be applied to...

Data mining models show great efficiency on acquiring knowledge for expert system classification. This study aimed at mining knowledge contained in landscape from multi-scale spatial data using decision tree learning model and evaluating the classification quality influenced by different scales of spatial data. Firstly, spatial data containing remote sensing images of different spatial and spec...

The rapid growth in the amount of spatial data available in Geographical Information Systems has given rise to substantial demand of data mining tools which can help uncover interesting spatial patterns. We advocate the relational mining approach to spatial domains, due to both various forms of spatial correlation which characterize these domains and the need to handle spatial relationships in ...

Data mining is the process of extracting implicit, valuable, and interesting information from large sets of data. Visualization is the process of visually exploring data for pattern and trend analysis, and it is a common method of browsing spatial datasets to look for patterns. However, the growing volume of spatial datasets make it difficult for humans to browse such datasets in their entirety...

Unlike the integration of geospatial data that deals with different geometric expressions, resolutions and actualities etc., integrating different domain datasets associated with the same geo-reference must consider additionally the specific domain knowledge and models. A number of spatial reference datasets and multidisciplinary datasets are selected as test examples. An analysis of data quali...

The advancement of GIS data models to allow the eŒective utilization of very large heterogeneous geographic databases requires a new approach that incorporates models of human cognition. The ultimate goal is to provide a cooperative human-computer environment for spatial analysis. We describe the pyramid framework as an example of this new approach within the context of some important aspects o...