The increasing use of digital media in daily life has resulted in a need for novel multimedia data analysis techniques. Case-based Reasoning (CBR) solves problems using the already stored knowledge, and captures new knowledge, making it immediately available for solving the next problem. Therefore, case-based reasoning can be seen as a method for problem solving, and also as a method to capture new experience and make it immediately available for problem solving. Therefore, CBR can mine sparse and big data. It can be seen as a learning and knowledge-discovery approach, since it can capture from new experience some general knowledge, such as case classes, prototypes and some higher-level concept. In this talk, we will explain the case-based reasoning process scheme. We will show what kinds of methods are necessary to provide all the functions for such a computer model. We will develop the bridge between CBR and Statistics and show how casebased reasoning can mine big and sparse data. Examples are being given based on multimedia applications. Finally, we will show recent new developments and we will give an outline for further work.
Perner, P. (2014). Mining sparse and big data by case-based reasoning. In Procedia Computer Science (Vol. 35, pp. 19–33). Elsevier B.V. https://doi.org/10.1016/j.procs.2014.08.081