Temporal Data Mining

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Temporal data mining means 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 or it may be primary biological data such as DNA, RNA and protein sequences. An important goal of temporal data mining is the search for patterns in the data that can help explaining its underlying structure. 

Temporal data mining deals with the harvesting of useful information from temporal data. It discusses the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction.

Examples of temporal data are regular time series (e.g., stock ticks, EEG), event sequences (e.g., sensor readings, packet traces, medical records, weblog data), and temporal databases (e.g., relations with timestamped tuples, databases with versioning). The common factor of all these sequence types is the total ordering of their elements. They differ on the type of primary information, the regularity of the elements in the sequence, and on whether there is explicit temporal information associated to each element (e.g., timestamps). There are several mining tasks that can be applied on.

Temporal data mining can be defined as “process of knowledge discovery in temporal databases that enumerates structures (temporal patterns or models) over the temporal data, and any algorithm that lists temporal patterns from, or fits models to, temporal data is a temporal data mining algorithm. The aim of temporal data mining is to discover temporal patterns, unexpected trends, or other hidden relations in the larger sequential data, which is composed of a sequence of nominal symbols from the alphabet known as a temporal sequence and a sequence of continuous real-valued elements known as a time series, by using a combination of techniques from machine learning, statistics, and database technologies. In fact, temporal data mining consists of three major works including representation of temporal data, definition of similarity measures and mining tasks.

According to techniques of data mining and theory of statistical time series analysis, the theory of temporal data mining may involve the following areas of investigation since a general theory for this purpose is yet to be developed. Temporal data mining tasks include;

  • Temporal data characterization and comparison
  • Temporal clustering analysis
  • Temporal classification
  • Temporal association rules
  • Temporal pattern analysis
  • Temporal prediction and trend analysis

New initiatives in healthcare and business organizations have increased the importance of temporal information in data today. It aims to be the incorporation of temporality in databases as well as temporal data representation, similarity computation, data classification, clustering, pattern discovery, and prediction. It is also used in the  temporal data mining in medicine and biomedical informatics, business and industrial applications, web usage mining, and spatiotemporal data mining. 

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