Explain some differences between mining association rules in multimedia databases and in transaction databases.

 To mine associations among multimedia objects, we can treat each image as a transaction and find frequently occurring patterns among different images. There are some differences between mining association rules in multimedia databases and in transaction databases.

First, an image may contain multiple objects, each with many features such as color, shape, texture, keyword, and spatial location, so there could be many possible associations. In many cases, a feature may be considered as the same in two images at a certain level of resolution, but different at a finer resolution level. Therefore, it is essential to promote a progressive resolution refinement approach.

Second, because a picture containing multiple recurrent objects is an important feature in image analysis, recurrence of the same objects should not be ignored in association analysis. For example, a picture containing two golden circles is treated quite differently from that containing only one.

Third, there often exist important spatial relationships among multimedia objects, such as above, beneath, between, nearby, left-of, and so on. These features are very useful for exploring object associations and correlations. Spatial relationships together with other content-based multimedia features, such as color, shape, texture, and keywords, may form interesting associations. Thus, spatial data mining methods and properties of topological spatial relationships become important for multimedia mining.