With the rapidly development of instruments and methods to obtain image data, great capacity for image data can be obtained. How to utilize these image data, how to get implicit, underlying, disciplinarian knowledge are urgent problems to resolve. This paper systemically, deeply analyzed and researched the intension and extension of the new concept of image(remote sensing image) data mining and knowledge discovery. Image data mining and knowledge discovery is the process of utilizing the theories and methods of spatial data mining, such as spatial clustering analysis, spatial association rule analysis, spatial serial analysis etc., to extract regular implicit useful information, image data relationships, spatial pattern etc. from image databases, or multi images, or multi sections of one image ", the concept is a dynamic concept, is a process, its aim is to promote the intelligentization of image processing, image mining can be done in image databases, or in one image, it developed based on some related technologies, the differences among image mining and these technologies are still not obvious. The paper thought image mining as a new theory and technology, it has its own particular researching content, own theories and technologies. The paper compared the concept of image mining with other related concepts, defined the research content and research system of image mining.This paper systemically, deeply analyzed the theory of Formal Concept Analysis(FCA). The theory of Formal Concept Analysis, also called Concept Lattice theory, is a powerful tools to analyze the process to produce concepts from data by mathematics formal methods, the process is similar to the process of data mining which produces knowledge from a great of data. So, Formal Concept Lattice is very suit to research the problems of data mining. Based on the theory, this paper researched the principle and algorithms of data mining, unified association rules, classification rules, clustering rules into the same form "A => B", and built the unified data mining framework which integrated association rules, classification rules, clustering rules together. The paper emphatically research the association rules algorithms based on FCA, and research out two kinds of improved algorithm of integrated the construction of Concept Lattice and the drawing of Hasse graph. The second kind of algorithm build the index tree, and layered the concept nodes based on the cardinally of the intent. Some experiments proved the two kinds of algorithms is better than the algorithm of Apriori, and the second algorithm is more fast than the first one.We understand the reality world in different layers and different granularity, so do for image mining, this involves in the granularity problem of image mining. This paper researched the quotient space theory, and used the theory's formal language to describe different granularity world, and combined the quotient space theory and Formal Concept Analysis theory to research the mechanism of image data mining and knowledge discovery, and produced a conceptsdriven theory framework ofVimage data mining and knowledge discovery. Based on the quotient space theory, the formally analysis system is built. In the formal analysis system, utilize the data mining algorithms based on FCA, extract concepts in different layers from the different granularity image worlds, and analyze the including and included relations among concepts, and mined out the implicit useful regular knowledge from image data.Image data mining and knowledge discovery is a very complex process. For a pile of complex image data, we can use the processing methods of hierarchy of quotient space theory, firstly decompose the complex problem into some subproblems according to regions, layers, image content, and carry out image data mining in different granularity, and then integrate these image mining results together. According to the positions of image data, image data can be divided into different regions, image data mining can be done in these different regions.
