Preface: pattern recognition and mining

Abstract

Natural computing, also called natural computation, refers to understanding computational processes observed in nature, and human-designed computing inspired by nature. It encompasses three classes of methods, namely, those that are inspired by the nature to develop novel problem-solving techniques; those that are used for modeling natural phenomena based on the use of computers; and those that employ natural materials such as molecules to compute with use of discovered models of relevant natural phenomena. Our understanding of nature, as well as the essence of computation, is enhanced if complex natural phenomena are analyzed in terms of computational processes. Characteristic for human-designed computing inspired by nature is the metaphorical use of concepts, principles, and mechanisms underlying natural systems. The processes occurring in nature can be viewed as different kinds of information processing. Self-assembly, selfreproduction, self-evolution, granulation, gene regulation networks, protein–protein interaction networks, active and passive biological transport networks, and gene assembly in unicellular organisms are some of the examples of such processes. Understanding the universe itself from the information processing point of view and engineering of semi-synthetic organisms are some efforts to understand biological systems. The most established classical nature-inspired models of computation are cellular automata, neural computation, evolutionary computation and granular computation. More recent computational systems abstracted from natural processes include artificial life, swarm intelligence, artificial immune systems, membrane computing, DNA computing, molecular computing, quantum computing, fractal geometry, and amorphous computing, among others. In fact, all major methods and algorithms are nature-inspired metaheuristic algorithms. Granulation is a process, among others, that is abstracted from natural phenomena. Granulation is inherent in human thinking and reasoning process. Granular computing (GrC) is a problem solving paradigm where computation and operations are performed on information granules, and it is based on the realization that precision is sometimes expensive and not very meaningful in modelling and controlling complex systems. This framework can be modeled with principles of neural networks, fuzzy sets and rough sets, both in isolation and integration, among other theories. GrC has been proven to be effective in intelligent information processing and data mining, and has a strong promise for Big data analysis. To reflect the current trends in the domain of natural computing and its application, this special issue of Natural Computing (Springer) on Pattern Recognition and Mining has been brought out. The issue contains nine contributory papers, six selected from those presented in 5th International Conference on Pattern Recognition and Machine Intelligence (PReMI 2013) and three out of a call for P. Maji (&) Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India e-mail: pmaji@isical.ac.in