Please enable JavaScript.
Coggle requires JavaScript to display documents.
DATA MINING (Spatial Mining: Spatial data mining is the application of…
DATA MINING
Spatial Mining: Spatial data mining is the application of data mining to spatial models. In spatial data mining, analysts use geographical or spatial information to produce business intelligence or other results. This requires specific techniques and resources to get the geographical data into relevant and useful formats.
-
-
-
Temporal Mining: Temporal data mining refers to the extraction of implicit, non-trivial, and potentially useful abstract information from large collections of temporal data. Temporal data are sequences of a primary data type, most commonly numerical or categorical values and sometimes multivariate or composite information.
Examples:
Regular Time Series (stock ticks, EEG)
event sequences (e.g., sensor readings, packet traces, medical records, weblog data)
temporal databases (e.g., relations with timestamped tuples, databases with versioning)
Web Mining: Web mining is the integration of information gathered by traditional data mining methodologies and techniques with information gathered over the World Wide Web.
-
-
-
Image Mining: image mining deals with the extraction of image patterns from a large collection of images.
Function Driven Framework: The function-driven framework spotlighted on the functionalities of different component modules to organize image mining systems
Image driven framework: An image mining system is often complicated because it employs various approaches and techniques ranging from image retrieval and indexing schemes to data mining and pattern recognition
-