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Unit 2: Data Mining Concept

for handling complex object structures, variable length records, semi-structured or unstructured notes
data, text and multimedia data, and database schemas with complex structures and dynamic
changes. Such database systems may raise many challenging research and implementation issues
for data mining and hence discussed in short as follows:
Multimedia Databases

Multimedia databases include video, images, audio and text media. They can be stored on
extended object-relational or object-oriented databases, or simply on a file system. Multimedia is
characterised by its high dimensionality, which makes data mining even more challenging. Data
mining from multimedia repositories may require computer vision, computer graphics, image
interpretation, and natural language processing methodologies.

spatial Databases

Spatial databases are databases that, in addition to usual data, store geographical information
like maps, and global or regional positioning. Such spatial databases present new challenges to
data mining algorithms.

figure 2.6: visualisation of spatial oLap

time-series Databases

Time-series databases contain time related data such stock market data or logged activities.
These databases usually have a continuous flow of new data coming in, which sometimes causes
the need for a challenging real time analysis. Data mining in such databases commonly includes
the study of trends and correlations between evolutions of different variables, as well as the
prediction of trends and movements of the variables in time.

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