Identifying structures in large data sets raises a number of problems. On the one hand, many methods cannot be applied to larger data sets, while, on the other hand, the results are often hard to interpret. We address these problems by a novel three-stage approach. First, we compute a small representation of the input data using a self-organizing map. This reduces the amount of data and allows us to create two-dimensional plots of the data. Then we use this preprocessed information to identify clusters of similarity. Finally, inductive learning methods are applied to generate sets of fuzzy descriptions of these clusters. This approach is applied to three case studies, including image data and real-world data sets. The results illustrate the generality and intuitiveness of the proposed method.
|Number of pages||6|
|Publication status||Published - 2001|
|Event||Joint 9th IFSA World Congress and 20th NAFIPS International Conference - Vancouver, BC, Canada|
Duration: 25 Jul 2001 → 28 Jul 2001
|Conference||Joint 9th IFSA World Congress and 20th NAFIPS International Conference|
|Period||25.07.2001 → 28.07.2001|