Applications of FP-Growth and Apriori Algorithm for Mining Fuzzified Spatial Dataset

Spatial data, also called geospatial data, is term needed to describe data linked to or containing knowledgeable data about a particular location on Earth’s surface. Spatial data mining's primary goal is to uncover hidden complicated information from spatial & non-spatial information in spite of their enormous quantity and find the spatial relations density. Spatial Data Mining techniques, however, continue to be an expansion of individuals utilized in standard data mining. Spatial Data is an extremely challenging area since enormous quantities of spatial data have been obtained from the remote sensed to the GIS (Geographic Information Systems), ecological estimation, computer cartography, planning and many more. In a given paper, we only focus on an essential type of spatial vagueness termed as spatial fuzziness. Spatial fuzziness intakes the property of several spatial objects in certainty which don’t contain boundaries of sharp type and interiors or whose boundaries as well as interiors can't be determined in precise form. This paper provides the method for finding fuzzy spatial data of association rule. Association rules provided valuable data in the assessment of important correlations observed in big databases. Compared to the previous research work, the current approach for there search highlights the superiority over the same dataset in terms of time taken and generated rules. The rules generated tell about the occurrence of attributes. The results show that the current research is more efficient than that of the previous work and also less time-consuming.

A Literature Review on Computer Cartography

Depending upon how we look at it, Computer Cartography can be considered now to be twenty years old (the first contour map was produced about twenty years ago), or a dozen years (the first major project in Computer Cartography, the Canadian Geographical Information System was started in 1963). This is not to say that we started out from scratch twelve or twenty years ago. On the contrary, we had all the tools and all the power from different disciplines, including Computer Science, Geography, Cartography and the Survey Sciences. If we assume that every discipline has some milestones in its development, it has to be said that in Computer Cartography these milestones did not happen or happened virtually unnoticed by the discipline.

Geospatial Data Reliability, Their Use In Crisis Situations

Spatial data are used not only for basic orientation in space but also as data for solving tasks connected with actual decisions, e.g. geographic impact on combat and non-combat army activities in given environments, predictions of landscape damage under extreme meteorological conditions or emergencies, in cases of military threats to the state, etc. in many tasks the source data are combined and based on mathematically or procedurally described processes, new data are created. Currently, computer equipment, geographic information systems (GIS), remote sensing, computer cartography, etc. in the hands of professionals, along with expert systems, can help much faster, more reliable and more efficient leadership of crisis management. When employing spatial data and information in decision-making processes, complex knowledge of their values is the prerequisite for assessing the credibility and accuracy of decisions made. By implementing the methods of value analysis and mathematical modeling it is possible to create an assessment system of spatial data complex usability. By comparing costs necessary for different variants of enhancement or for adjustment of database quality it is possible to optimize both the total usability and the costs put in securing the required data quality. Paper describes one possible way of creation of cost map.