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medical data, such as in the histories of patients’ medical visits. Patients are associated with both static properties, such as gender, and temporal properties, such as age, symptoms, or current medical treatments. Adapting this method to deal with temporal information leads to some different approaches. A possible extension is a new meaning for a typical association rule X ≥ Y. It states now that if X occurs, then Y will occur within time T. Stating a rule in this new form allows for controlling the impact of the occurrence of one event to the other event occurrence, within a specific time interval. In case of the sequential patterns framework some generalizations are proposed to incorporate minimum and maximum time-gap constraints between successive elements of a sequential pattern.

Mining continuous data streams is a new research topic related to temporal data mining that has recently received significant attention. The term “data stream” pertains to data arriving over time, in a nearly continuous fashion. It is often a fast-changing stream with a huge number of multidimensional data (Fig. 12.24). Data are collected close to their source, such as sensor data, so they are usually with a low level of abstraction. In streaming data-mining applications, the data are often available for mining only once, as it flows by. That causes several challenging problems, including how to aggregate the data, how to obtain scalability of traditional analyses in massive, heterogeneous, nonstationary data environment, and how to incorporate incremental learning into a data-mining process. Linear, single-scan algorithms are still rare in commercial data-mining tools, but also still challenged in a research community. Many applications, such as network monitoring, telecommunication applications, stock market analysis, bio-surveillance systems, and distribute sensors depend critically on the efficient processing and analysis of data streams. For example, a frequent itemset-mining algorithm over data stream is developed. It is based on an incremental algorithm to maintain the FP stream, which is a tree data structure to represent the frequent itemsets and their dynamics in time.

Figure 12.24. Multidimensional streams.

Ubiquitous Data Mining (UDM) is an additional new field that defines a process of performing analysis of data on mobile, embedded, and ubiquitous devices. It represents the next generation of data-mining systems that will support the intelligent and time-critical information needs of mobile users and will facilitate “anytime, anywhere” data mining. It is the next natural step in the world of ubiquitous computing. The underlying focus of UDM systems is to perform computationally intensive mining techniques in mobile environments that are constrained by limited computational resources and varying network characteristics. Additional technical challenges are as follows: How to minimize energy consumption of the mobile device during the data mining process; how to present results on relatively small screens; and how to transfer data mining results over a wireless network with a limited bandwidth?

12.3 SPATIAL DATA MINING (SDM)

SDM is the process of discovering interesting and previously unknown but potentially useful information from large spatial data sets. Spatial data carries topological and/or distance information, and it is often organized in databases by spatial indexing structures and accessed by spatial access methods. The applications covered by SDM include geomarketing, environmental studies, risk analysis, remote sensing, geographical information systems (GIS), computer cartography, environmental planning, and so on. For example, in geomarketing, a store can establish its trade area, that is, the spatial extent of its customers, and then analyze the profile of those customers on the basis of both their properties and the area where they live. Simple illustrations of SDM results are given in Figure 12.25, where (a) shows that a fire is often located close to a dry tree and a bird is often seen in the neighborhood