Name
Abstract | ||
|---|---|---|
A c-fuzzy means algorithm is described. The algorithm learns fuzzy prototypes to represent data sets and is based on ideas taken from mass assignment theory. The potential of this approach for both unsupervised and supervised learning is illustrated by its application to a number of benchmark and model problems In many of the emerging information technologies there is a clear need for automated learning from databases. For instance, data mining methods attempt to extract useful general knowledge from the implicit patterns contained in databases. Such methods are of particular interest to supermarkets and other businesses that collect large amounts of data relating to customers and their purchasing habits in the hope of learning descriptions of certain types of consumer. Machine learning approaches learn models of complex systems capable of accurate prediction. Such methods have application to classification problems as well as vision, function approximation and control. For the areas described above knowledge representation plays an important role in the learning process. In data mining the objective is to learn useful general rules and hence it is a requirement that these take a form that can be relatively easily understood by humans. Similarly, for machine learning it is advantageous that the models inferred are transparent as this gives insight into the behaviour of the system as well as making it possible to evaluate the appropriateness of the model. On the other hand it must be the goal of any learning processes to obtain rules, facts and models that are efficient at |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/FUZZY.2000.838652 | Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference |
Keywords | DocType | Volume |
fuzzy set theory,inference mechanisms,knowledge representation,learning (artificial intelligence),c-fuzzy means algorithm,fuzzy set theory,knowledge representation,mass assignment theory,prototype induction,supervised learning,unsupervised learning | Conference | 1 |
ISSN | ISBN | Citations |
1098-7584 | 0-7803-5877-5 | 2 |
PageRank | References | Authors |
0.41 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. F. Baldwin | 1 | 8 | 1.64 |
| Jonathan Lawry | 2 | 172 | 19.06 |