Qing He – författare

Data Mining is the process of posing queries and extracting useful information, patterns and trends previously unknown from large quantities of data [Thu, 00]. It is the process where intelligent tools are applied in order to extract data patterns [JM, 01]. This encompasses a number of different technical approaches, such as cluster analysis, learning classification and association rules, and finding dependencies. Agents are defined as software entities that perform some set of tasks on behalf of users with some degree of autonomy. This research work deals about developing a automated data mining system which encompasses the familiar data mining algorithms using intelligent agents in object oriented databases and proposing a framework. Because the data mining system uses the intelligent agents, a new user will be able to interact with the data mining system without much data mining technical knowledge. This system will automatically select the appropriate data mining technique and select the necessary field needed from the database at the appropriate time without expecting the users to specify the specific technique and the parameters. Also a new framework is proposed for incorporating intelligent agents with automated data mining. One of the major goals in developing this system is to give the control to the computer for learning automatically by using intelligent agents.

Data Mining is the process of posing queries and extracting useful information, patterns and trends previously unknown from large quantities of data [Thu, 00]. It is the process where intelligent tools are applied in order to extract data patterns [JM, 01]. This encompasses a number of different technical approaches, such as cluster analysis, learning classification and association rules, and finding dependencies. Agents are defined as software entities that perform some set of tasks on behalf of users with some degree of autonomy. This research work deals about developing a automated data mining system which encompasses the familiar data mining algorithms using intelligent agents in object oriented databases and proposing a framework. Because the data mining system uses the intelligent agents, a new user will be able to interact with the data mining system without much data mining technical knowledge. This system will automatically select the appropriate data mining technique and select the necessary field needed from the database at the appropriate time without expecting the users to specify the specific technique and the parameters. Also a new framework is proposed for incorporating intelligent agents with automated data mining. One of the major goals in developing this system is to give the control to the computer for learning automatically by using intelligent agents.

This book deals with alignment optimization models for planning rail transit. It shows how to set up a 3D GIS scene for alignment interaction design, including location calculation of an alignment, alignment expression in a 3D scene, and the spacial relations between geographic objects, horizontal alignment, and vertical alignment.

This book deals with alignment optimization models for planning rail transit. After a general introduction to the basics of alignment optimization theory, it presents different alignment optimization methods to deal with different situations. It shows how to set up a 3D GIS scene for alignment interaction design, including location calculation of an alignment, alignment expression in a 3D scene, and the spacial relations between geographic objects, horizontal alignment, and vertical alignment. Further, it presents methods for solving more complex alignment optimization models, and shows for each different rail transit situation, how to calculate investment, energy consumption, and environmental influence. All in all, this book offers an interesting and timely reading to both researchers and professionals in the field of optimization theory, transportation planning, and GIS.