Research On Several Key Technologies In Spatial Data Mining

Spatial data mining of spatial databases is the extraction of implicit knowledge, spatial relations and discovery of interesting characteristics and patterns which are not explicitly represented in spatial databases.The technique can play an important role in understanding spatial data and capturing the intrinsic relationships between spatial and non-spatial data. In recent years Geography Information System(GIS) has been used in many fields.It has become one of the important tasks, which need be studied currently ,because the amount of spatial data obtained from GIS and other sources has been growing tremendously.It is under such background that the author effectively studies the corresponding several key technology on the spatial data mining, and systematieally discussed the basic theory of spatial data mining in this thesis. The achievements of this dissertation can be concluded as follows:1 .Based on the present research, the author studis the measurement of spatial distance as a basic rule of spatial computation. By extending the method of spatial weighted matrix, the author analyse forth the conception of spatial entity association matrix, and analyse the method of their establishment and offers new basic tools for SDM.2.The author describe the mixture model for model-based clustering and the classic form of the Expectation Maximization(EM) algorithm. Despite EM's wide-spread popularity, practical usefulness of EM is often limited by computational inefficiency. EM makes a pass through all of the available data in every iteration. Thus, if the size of the data set is large, every iteration can be computationally intensive. the author introduces the Increasing EM(IEM) algorithm for fast computation based on random sub-sampling.Using only a subset rather than the entire database allows for significant computational improvements since many fewer data points need to be evaluated in every iteration. The author also argue that one can choose the subsets intelligently by appealing to EMs highly-appreciated likelihood-judgement condition and increment factor. IEM algorithm can lead to significant computational improvements without sacrificing accuracy of the results.3.EM algorithm is inappropriate spatial clustering to requires conside-ration of spatial information. Although neighborhood EM (NEM) algorithm incorporates a spatial penalty term, it needs more iterations in every E-step. To incorporate spatial information while avoid too much additional computation, the author proposed Mixed Increasing NEM(MNEM) algorithm that combines EM and NEM. In MNEM, the author first train data based on random sub-sampling in EM till the likelihood-judgement condition begins to decrease,and update sub-sampling .Then training is turned to NEM and runs one iteration of algorithm. Because of this cross train of cycle, MNEM algorithm' computational complexity is decreased and capability is advanced.4.The multilevel spatial association rules are discovered from a spatial database in which all items of predicates are described by a set of relevant attributes. A multilevel association pattern is a frequent predicate-set in which all items constituting predicates is at a certain concept level, respectively. In this paper, we present a new approach to discover strong multilevel spatial association rules in spatial databases by storing separately the spatial predicates acquired by the execution of spatial query and some efficient spatial algorithms. Then we construct parent element table and frequent class-matched table based on the spatial relations denoted as relation table R.This makes the discovery of multilevel spatial association rules easy and efficient.5.Directional information is one of the most important types of information in an image database, and the Nine Direction Lower-Triangular Martix(9DLT) representation is fundamental in this method. Therefore, we propose a novel spatial mining algorithm, called 9DLT Image Mining(9DIM), to mine the spatial association rules from an image database, where every image is represented by the 9DLT representation.Image mode database is similar to Transaction Database because every 9DLT character string express a transaction.According to relation mode among the image object, we construct frequent k-1 (k>2) mode database. By way of construction of frequent k mode tree based frequent k-1 (k>2) mode database, we can mine frequent mode all of object. Since our proposed algorithm prunes most of impossible candidates, it is more efficient than the Apriori algorithm.