Databases Reference
In-Depth Information
13
Data Mining Trends
and Research Frontiers
As a young research field
, data mining has made significant progress and covered a broad spec-
trum of applications since the 1980s. Today, data mining is used in a vast array of
areas. Numerous commercial data mining systems and services are available. Many chal-
lenges, however, still remain. In this final chapter, we introduce the mining of complex
data types as a prelude to further in-depth study readers may choose to do. In addi-
tion, we focus on trends and research frontiers in data mining. Section 13.1 presents an
overview of methodologies for mining complex data types, which extend the concepts
and tasks introduced in this topic. Such mining includes mining time-series, sequential
patterns, and biological sequences; graphs and networks; spatiotemporal data, including
geospatial data, moving-object data, and cyber-physical system data; multimedia data;
text data; web data; and data streams. Section 13.2 briefly introduces other approaches
to data mining, including statistical methods, theoretical foundations, and visual and
audio data mining.
In Section 13.3, you will learn more about data mining applications in business and
in science, including the financial retail, and telecommunication industries, science and
engineering, and recommender systems. The social impacts of data mining are discussed
in Section 13.4, including ubiquitous and invisible data mining, and privacy-preserving
data mining. Finally, in Section 13.5 we speculate on current and expected data mining
trends that arise in response to new challenges in the field.
13.1
Mining Complex Data Types
In this section, we outline the major developments and research efforts in mining com-
plex data types. Complex data types are summarized in Figure 13.1. Section 13.1.1
covers mining sequence data such as time-series, symbolic sequences, and biological
sequences. Section 13.1.2 discusses mining graphs and social and information networks.
Section 13.1.3 addresses mining other kinds of data, including spatial data, spatiotem-
poral data, moving-object data, cyber-physical system data, multimedia data, text data,
