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What is the difference between Spatial and Temporal Data Mining?
Spatial Data Mining
Spatial data mining is the application of data mining to spatial models. In spatial data mining, analysts use geographical or spatial records to create business intelligence or multiple results. This needed specific techniques and resources to obtain the geographical information into relevant and useful formats.
The evolution of spatial data and extensive usage of spatial databases has governed spatial knowledge discovery. Spatial data mining can be learned as a process that decides some astonishing and hypothetically valuable patterns from spatial databases.
There are several challenges involved in spatial data mining include recognizing patterns or discovering objects that are relevant to the questions that drive the research project. Analysts can be viewed in a huge database field or other completely large data set to find the relevant data, using GIS/GPS tools or the same systems.
The objective of a spatial data mining project is to distinguish the data to construct real, actionable patterns to present, excluding things such as statistical coincidence, randomized spatial modelling, or irrelevant outcomes. One way analysts can do this is by combing through data viewing for "same-object" or "object-equivalent" models to support accurate comparisons of multiple geographic areas.
Temporal Data Mining
Temporal data mining defines the process of extraction of non-trivial, implicit, and potentially essential data from large sets of temporal data. Temporal data are a series of primary data types, generally numerical values, and it deals with gathering beneficial knowledge from temporal data.
Temporal data mining aims to find temporal patterns, unexpected trends, or other hidden relations in the higher sequential data, which is composed of a sequence of nominal symbols from the alphabet called a temporal sequence and a sequence of continuous real-valued components known as a time series, by using a set of techniques from machine learning, statistics, and database technologies.
Temporal data mining is composed of three major works as the description of temporal data, the definition of similarity measures, and mining services.
Temporal Data Mining includes processing time sequence, generally sequences of records, which calculate values of the similar attribute at a sequence of multiple time points. Pattern matching using such information, where we are searching for specific patterns of interest, has attracted considerable interest in current years.
Temporal Data Mining can involve the exploitation of effective techniques of data storage, quick processing, and quick retrieval methods that have been developed for temporal databases.
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