04-10-2012, 10:43 AM
Applications of Data Mining
Applications of Data Mining.pptx (Size: 44.08 KB / Downloads: 48)
Data Mining in Other Scientific Applications :-
Previously, most scientific data analysis tasks tended to handle relatively small and homogeneous data sets.
“formulate hypothesis, build model, and evaluate results”
Today, data collection and storage technologies have recently improved. Resulted in accumulation of huge volumes of high dimensional data, stream data, and heterogeneous data, containing rich spatial and temporal information.
Scientific applications are shifting from the “hypothesis-and-test” paradigm toward a “collect and store data, mine for new hypotheses, confirm with data or experimentation” process.
Data collection and storage technologies have improved., so that scientific data can be amassed at much higher speeds and lower costs. This resulted in accumulation of huge volumes of data.
Vast amount of data have been collected from scientific domains. Large data sets are being generated due to fast numerical simulations in various fields.
Challenges brought about by emerging scientific applications of data mining:-
Data warehouse and data processing:
For scientific applications in general, methods are needed for integrating data from heterogeneous sources and for identifying events.
For climate and ecosystem data , for instance, the problem is that there are too many events in the spatial domain and too few in temporal domain.
Mining complex data types:
scientific data sets are heterogeneous in nature, typically involving semi-structured and un-structured data.
robust methods are needed for handling spatio-temporal data and complex graphic relationships.
Graph-based mining :
difficult to model several physical phenomena and processes. Alternatively, labeled graphs may be used to capture many spatial, topological, geometric and other relational characteristics present in scientific data sets.
In graph modeling, each object to be mined is represented by a vertex in a graph, and edges b/w vertices represent relatioships b/w objects.
Visualization tools and domain specific knowledge:
high-level graphical user interfaces and visualization tools are required for scientific data mining s/m. these should be integrated with existing domain specific information s/m & db s/m to guide researchers and general users in searching for patterns, interpreting and visualizing discovered patterns and using discovered knowledge in their decision making.