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Monday, August 27, 2012

PowerPoint Presentation On Mining Complex Types of Data

PPT On Mining Complex Types of Data

Presentation Transcript:
1.Data Mining: Concepts and Techniques

2.Mining Complex Types of DataMultidimensional analysis and descriptive mining of complex data objects
Mining spatial databases
Mining multimedia databases
Mining time-series and sequence data
Mining text databases
Mining the World-Wide Web

3.Mining Complex Data Objects: Generalization of Structured Data
Set-valued attribute
Generalization of each value in the set into its corresponding higher-level concepts
Derivation of the general behavior of the set, such as the number of elements in the set, the types or value ranges in the set, or the weighted average for numerical data
E.g., hobby = {tennis, hockey, chess, violin, nintendo_games} generalizes to {sports, music, video_games}
List-valued or a sequence-valued attribute
Same as set-valued attributes except that the order of the elements in the sequence should be observed in the generalization

4.Generalizing Spatial and Multimedia Data
Spatial data:
Generalize detailed geographic points into clustered regions, such as business, residential, industrial, or agricultural areas, according to land usage
Require the merge of a set of geographic areas by spatial operations
Image data:
Extracted by aggregation and/or approximation
Size, color, shape, texture, orientation, and relative positions and structures of the contained objects or regions in the image
Music data:
Summarize its melody: based on the approximate patterns that repeatedly occur in the segment
Summarized its style: based on its tone, tempo, or the major musical instruments played

5.Generalizing Object Data
Object identifier: generalize to the lowest level of class in the class/subclass hierarchies
Class composition hierarchies
generalize nested structured data
generalize only objects closely related in semantics to the current one
Construction and mining of object cubes
Extend the attribute-oriented induction method
Apply a sequence of class-based generalization operators on different attributes
Continue until getting a small number of generalized objects that can be summarized as a concise in high-level terms
For efficient implementation
Examine each attribute, generalize it to simple-valued data
Construct a multidimensional data cube (object cube)
Problem: it is not always desirable to generalize a set of values to single-valued data

6. Thank You.

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