Databases Reference
In-Depth Information
ing computational approaches. The study of the recognition process based on
computational theories and the biological behavior of the nervous system can
be traced back to the 1950s, when digital computers started being used for
information processing. The ability to recognize and extract valuable informa-
tion from raw data has motivated extensive research on pattern recognition
techniques. Such techniques aspire to emulate the behavior of neural systems
in living organisms.
To fully understand the concept of pattern recognition, there is a need to
differentiate between some of the terms that are commonly used interchange-
ably, namely pattern recognition, data mining, and pattern classification.
Pattern recognition is the process of identifying an object or entity based on
its descriptions and a set of measurements, commonly referred to as a pattern.
Keeping with the previous example, a tree can be characterized by its vertical
cylindrical shape, leaves, bark, and branches. In pattern recognition, we use
these features to identify and differentiate a tree from other objects, such as
a chimney or water reservoir.
To obtain useful information from data, it is important for applications to
extract features or patterns. Pattern extraction from data is commonly known
as data mining and involves uncovering patterns, associations, anomalies, in-
teresting data structures, and traces of events. Recognition of patterns plays
an important role in data mining applications in a variety of fields, including
the life and physical sciences, economics, finance, and engineering.
Pattern classification is the process of assigning an object or entity to a
class that shares similar characteristics or features. For example, biological
taxonomy uses pattern classification to identify and label individuals as a
class of species that have similar characteristics and behaviors.
The aim of any pattern recognition scheme is to achieve high recall ac-
curacy for any recognition problem. However, almost every approach has to
substantially increase its algorithmic complexity to accommodate this goal.
Some promising approaches in assimilating and comprehending the function-
alities of biological nervous systems have been proposed. Nevertheless, the
highly cohesive procedures and processing-centric algorithmic design of these
approaches may limit the capabilities of such approaches. Because require-
ments for the intensive collection and retrieval of data are appearing as a
consequence of the data deluge phenomenon, it is important that we also
consider the recognition process from the perspective of scalability.
1.2 Recognition at a Large Scale
Provided that we have solved the scalability problem, the Internet provides
levels of connectivity and complexity that bear a resemblance to the human
