The areas covered by the term Computational Intelligence (CI) are also known under the name Soft Computing (SC), to be distinguished by the Operations Research (OR), also known as hard computing. The two areas are connected by the problem domains they are applied in, but while OR algorithms usually come with strict conditions on the scope of applicability and proven guarantees for a solution (or even an optimal solution), SC puts no conditions on the problem but also provides no guarantees for success, a deficiency which is compensated by the robustness of the methods. SC refers to a collection of new computational techniques in computer science, artificial intelligence, machine learning and many applied and engineering areas, e.g. neural networks, fuzzy systems, evolutionary computation, Bayesian network and chaos theory.
Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. The aim of clustering is to organize a collection of objects (usually represented as vectors of measurements, or points in a multidimensional space) into different groups (called clusters) according to some defined similarity measure, which determines the cluster shape. There have been several suggestions for a measure of similarity between two clusters. Such a measure can be used to compare how well different data clustering algorithms perform on a set of data. In biology clustering has two main applications in the fields of computational biology and bioinformatics; it is used to build groups of genes with related expression patterns and to group homologous sequences into gene families. Market researchers use cluster analysis to partition the general population of consumers into market segments and to better understand the relationships between different groups of consumers/potential customers. In the study of social networks, clustering may be used to recognize communities within large groups of people. In image segmentation, clustering can be used to divide a digital image into distinct regions for border detection or object recognition. Many data mining applications involve partitioning data items into related subsets, e.g. a common application is the division of documents, such as World Wide Web pages, into genres.