空间数据挖掘方法及问题

Method Development about Spatial Data Mining and Its Problem Analysis Hu Shengwu School of Surveying and land information engineering Henan Polytechnic University Henan Jiaozuo,454000 ,China hushengwuzhu@163.com

Abstract-The people gain more and more spatial data as spatial information technologies develop especially Remote Sensing technology and surveying and Mapping technology, database technology, network technology. But there is a bizarre phenomenon that “data rich, knowledge lack”. For the sake of solving the problem, we must carry through spatial data mining, therefore it is very important to spatial data mining. The paper researches two aspects that are methods and problems of spatial data mining. The paper studies characteristic and using range of every kind method and points out localization and fault about spatial data mining at present; detailed works over existing problems of spatial data mining now, educes that spatial data mining is faultiness and needs more research

Keywords-Spatial Data Mining; Method; Problem, Analysis I. PREFACE The people gain more and more spatial data as spatial information technologies develop especially Remote Sensing technology and surveying and mapping technology, database technology, network technologyDŽ A strange thing comes forth that “data rich, knowledge lack” , that is to say, people have many spatial data but feel lack of spatial data knowledge. It is very important how to discover knowledge from a mass of spatial data, and so spatial data mining is research hotspot at present. The paper sums up methods and researches problems about spatial data mining at present.

II. DEFINITION OF SPATIAL DATA MINING

The spatial data mining and the general data mining have not only relation but also difference. The two concepts are data mining (Data Mining, is called DM) and discover the knowledge (Discovery for Spatial Database, is called KDD) usually are confused. Some scholars take DM as a core process of KDD and think KDD process includes data preparation and interpretation and evaluation of discovery result. Some people think that the essence of the two concepts is same, DM only frequently is used in statistics , data analysis and information system, but KDD is widely used in artificial intelligence and machine learning and so on domain. Also some people think the two concepts are separated with difficulty, should take a whole as using, But the terminology "the data mining" compared to "in the database discovered the knowledge" and "the data mining and the knowledge discover" is concision, therefore is widely popular.

The spatial data mining (Spatial Data Mining, is called SDM), or called the knowledge discovery from the spatial database(Knowledge Discovery from Spatial Database), its definition is: in the spatial database and in the data warehouse foundation, comprehensively utilizes statistical method, the pattern recognition technology, the artificial intelligence method, the neutral network technology, Rough set, the fuzzy mathematics, the machine learning, the expert systematic, visualization technology and other correlative information technologies taken as the measure, extracts knowledge that is credible ,novel, interesting, hidden, unknown, latent, useful and finally comprehensible from the massive spatial data, the management data, the fare data or remote sensing data, thus opens out essence rule containing spatial data, inherence relation and development tendency , realizes knowledge is automatically or semi-automatically gain, provides the suggestion for the management and the fare decision-making[1].

III. THE SPATIAL DATA MINING RESEARCH STATUS

GENERALIZATION Since 1989 the 11th session of artificial intelligence international combination conference proposed for the first time the data mining concept, all kinds of organization of the overseas have processed the massive research work in the data mining domain. Until now, the data mining research has already gained great progress in the relational database and business database[2] , these representative work includes: using object the attribute induction method discovered the characteristic rule and the discrimination rule in the relational database [3], discovered the connection rule in business database[4], from the large-scale database discovered the multi-level connective rule[5] , carries with the classification based on the multilayered connection[6] , processes optimization of clustering analysis based on the distance and the density and so on[7]. Many famous databases and the data warehouse suppliers, the statistical analysis software developer, the correlation personnel and the research institute and so on invest in abundance studies and exploitation strength, develops some data mining commercial systems and the prototype systems[8]. The spatial data mining research compared with the general data mining is late, but it has aroused the widespread interest in recent years. Canadian Simon Fraser university, German Munich university , many universities and the research institute of American and Australia and so on, all have the achievement report about the spatial data mining[9][10]. These researchers mostly have the computer science background, they generally take the spatial data