Introduction: Aggregation and approximation are another important means of generalization. They are especially useful for generalizing attributes with large sets of values, complex structures, and spatial or multimedia data.
Let’s take spatial data as an example. We would like to generalize detailed geographic points into clustered regions, such as business, residential, industrial, or agricultural areas, according to land usage. Such generalization often requires the merge of a set of geographic areas by spatial operations, such as spatial union or spatial clustering methods. Aggregation and approximation are important techniques for this form of generalization. In a spatial merge, it is necessary to not only merge the regions of similar types within the same general class but also to compute the total areas, average density, or other aggregate functions while ignoring some scattered regions with different types if they are unimportant to the study. Other spatial operators, such as spatial-union, spatial-overlapping, and spatial-intersection (which may require the merging of scattered small regions into large, clustered regions) can also use spatial aggregation and approximation as data generalization operators.
Example: Spatial aggregation and approximation. Suppose that we have different pieces of land for various purposes of agricultural usage, such as the planting of vegetables, grains, and fruits. These pieces can be merged or aggregated into one large piece of agricultural land by a spatial merge. However, such a piece of agricultural land may contain highways, houses, and small stores. If the majority of the land is used for agriculture, the scattered regions for other purposes can be ignored, and the whole region can be claimed as an agricultural area by approximation.
A multimedia database may contain complex texts, graphics, images, video fragments, maps, voice, music, and other forms of audio/video information. Multimedia data are typically stored as sequences of bytes with variable lengths, and segments of data are linked together or indexed in a multidimensional way for easy reference.
Generalization on multimedia data can be performed by recognition and extraction of the essential features and/or general patterns of such data. There are many ways to extract such information. For an image, the size, color, shape, texture, orientation, and relative positions and structures of the contained objects or regions in the image can be extracted by aggregation and/or approximation. For a segment of music, its melody can be summarized based on the approximate patterns that repeatedly occur in the segment, while its style can be summarized based on its tone, tempo, or the major musical instruments played. For an article, its abstract or general organizational structure (e.g., the table of contents, the subject and index terms that frequently occur in the article, etc.) may serve as its generalization.
In general, it is a challenging task to generalize spatial data and multimedia data in order to extract interesting knowledge implicitly stored in the data. Technologies developed in spatial databases and multimedia databases, such as spatial data accessing and analysis techniques, pattern recognition, image analysis, text analysis, content-based image/text retrieval and multidimensional indexing methods, should be integrated with data generalization and data mining techniques to achieve satisfactory results
