Data Mining: The Textbook



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The temporal attribute is contextual, whereas the spatial attributes are behavioral:

Strictly speaking, this kind of data can also be considered time-series data. However, the spatial nature of the behavioral attributes also provides better interpretability and more focused analysis in many scenarios. The most common form of this data arises in the context of trajectory analysis.

It should be pointed out that any 2- or 3-dimensional time-series data can be mapped onto trajectories. This is a useful transformation because it implies that trajectory mining algorithms can also be used for 2- or 3-dimensional time-series data. For example, the Intel Research Berkeley data set [556] contains readings from a variety of sensors. An example of a pair of readings from a temperature and voltage sensor are illustrated in Figs. 1.2a and b, respectively. The corresponding temperature–voltage trajectory is illustrated in Fig. 1.2c. Methods for spatial and spatiotemporal data mining are discussed in Chap. 16.


1.3.2.4 Network and Graph Data


In network and graph data, the data values may correspond to nodes in the network, whereas the relationships among the data values may correspond to the edges in the network. In some cases, attributes may be associated with nodes in the network. Although it is also possible to associate attributes with edges in the network, it is much less common to do so.


Definition 1.3.5 (Network Data) A network G = (N, A) contains a set of nodes N and a set of edges A, where the edges in A represent the relationships between the nodes. In



1.3. THE BASIC DATA TYPES

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  1. Temperature-voltage trajectory

Figure 1.2: Mapping of multivariate time series to trajectory data





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