Geostatistical Analysis Research Using Adult Data Set

With the great progress of microelectronics and other relating information technologies, together with the still broadening applications of computers in a vast range of businesses and industries, large databases containing mixedmode data are becoming quite commonplace. Today, large databases contain various modes of collected data related to different components of a complex real world system. Their use is not necessarily confined to classifications. Many of them may not have clearly-defined class labels or even any explicit class information at all. Indeed, there are many different reasons to determine or discover all patterns, to achieve any comprehensive analysis and understanding of the information within the data spaces. In the past, data mining or pattern discovery has by and large been developed fundamentally for categorical databases. All of the classification rules have been found from pre-labeled data samples. For a large mixed-mode database, how to discredit its continuous data into interval events is still a practical approach. If there are no class labels for the database, we have no helpful correlation references to such task actually a large relational database may contain various correlated attribute clusters. To handle these kinds of problems, we first have to partition the databases into sub-groups of attributes containing some sort of correlated relationship. This process has become known as attribute clustering, and it is an important way to reduce our search in looking for or discovering patterns Furthermore, once correlated attribute groups are obtained, from each of them, we could find the most representative attribute with the strongest interdependence with all other attributes in that cluster, and use it as a candidate like a class label of that group. That will set up a correlation attribute to drive the discretization of the other continuous data in each attribute cluster. This thesis provides the theoretical framework, the methodology and the computational system to achieve that goal. In validating the premises proposed in the Paper, extensive experiments using UCI Expository Data of various types were performed to verify each of the fine points conceived. To demonstrate the usefulness for solving real world problems, the developed methodology is applied to Adult databases from the real world.