Clustering Co-occurrence of Maximal Frequent Patterns in Streams
| dc.creator | de Graaf, Edgar H. | |
| dc.creator | Kok, Joost N. | |
| dc.creator | Kosters, Walter A. | |
| dc.date | 2007-05-04 | |
| dc.date.accessioned | 2026-07-07T07:59:30Z | |
| dc.date.available | 2026-07-07T07:59:30Z | |
| dc.description | One way of getting a better view of data is using frequent patterns. In this paper frequent patterns are subsets that occur a minimal number of times in a stream of itemsets. However, the discovery of frequent patterns in streams has always been problematic. Because streams are potentially endless it is in principle impossible to say if a pattern is often occurring or not. Furthermore the number of patterns can be huge and a good overview of the structure of the stream is lost quickly. The proposed approach will use clustering to facilitate the analysis of the structure of the stream. A clustering on the co-occurrence of patterns will give the user an improved view on the structure of the stream. Some patterns might occur so much together that they should form a combined pattern. In this way the patterns in the clustering will be the largest frequent patterns: maximal frequent patterns. Our approach to decide if patterns occur often together will be based on a method of clustering when only the distance between pairs is known. The number of maximal frequent patterns is much smaller and combined with clustering methods these patterns provide a good view on the structure of the stream. | |
| dc.identifier | https://arxiv.org/abs/0705.0588 | |
| dc.identifier | http://arxiv.org/abs/0705.0588 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/128389 | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Data Structures and Algorithms | |
| dc.title | Clustering Co-occurrence of Maximal Frequent Patterns in Streams | |
| dc.type | text |