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What is the 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.
The objective of temporal data mining is to find temporal patterns, unexpected trends, or several hidden relations in the higher sequential data, which is composed of a sequence of nominal symbols from the alphabet referred to as a temporal sequence and a sequence of continuous real-valued components called a time series, by utilizing a set of approaches from machine learning, statistics, and database technologies.
Temporal data mining is composed of three major works such as the description of temporal data, representation of similarity measures, and mining services.
Temporal Data Mining includes processing time series, generally sequences of data, which compute values of the same attribute at a sequence of multiple time points. Pattern matching using such information, where it is searching for specific patterns of interest, has attracted considerable interest in current years.
Temporal Data Mining can include the exploitation of efficient techniques of data storage, quick processing, and quick retrieval methods that have been advanced for temporal databases.
Temporal data mining is an individual phase in the process of knowledge discovery in temporal databases that calculate temporal patterns from or fit models too, temporal data is a temporal data mining algorithm.
Temporal data mining is concerned with the analysis of temporal data and for discovering temporal patterns and consistencies in sets of temporal information. It also allows the possibility of computer-driven, automatic exploration of the data. There are various tasks in temporal mining which are as follows −
- Data characterization and comparison
- Clustering analysis
- Classification
- Association rules
- Pattern analysis
- Prediction and trend analysis
Temporal data mining has led to a new way of interacting with a temporal database and specifying queries at a much more abstract level than say, temporal structured query language permits. It also facilities data exploration for problems that are due to multiple and multi-dimensionality.
The basic goal of temporal classification is to predict temporally related fields in a temporal database based on other fields. The problem, in general, is cast as deciding the general value of the temporal variable being predicted given the different fields, the training data in which the target variable is given for each observation, and a set of assumptions representing one’s prior knowledge of the problem. Temporal classification techniques are associated with the complex problem of density estimation.
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