منابع مشابه
Discovering Geometric Frequent Subgraphs
As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets, is to use a graph to model the database objects. Within that model, the problem of finding frequent patterns becomes that of discov...
متن کاملDiscovering Frequent Geometric Subgraphs
Data mining-based analysis methods are increasingly being applied to datasets derived from science and engineering domains that model various physical phenomena and objects. In many of these datasets, a key requirement for their effective analysis is the ability to capture the relational and geometric characteristics of the underlying entities and objects. Geometric graphs, by modeling the vari...
متن کاملA new proposal for graph classification using frequent geometric subgraphs
Geometric graph mining has bees identified as a need in many applications. This technique detect patterns with some tolerance under a geometric transformation. To meet this need, some graph miners have been developed for detecting frequent geometric subgraphs. However, there are few works for applying this kind of geometric patterns as feature for classification tasks. In this paper, a new geom...
متن کاملMining Frequent Most Informative Subgraphs
The main practical problem encountered with frequent subgraph search methods is the tens of thousands of returned graph patterns that make their visual analysis impossible. In order to face this problem, are introduced a very restricted family of relevant graph patterns called the most informative patterns along with an algorithm to mine them and associated experimental results. In graph-based ...
متن کاملDiscovering frequent pattern pairs
Cubes and association rules discover frequent patterns in a data set, most of which are not significant. Thus previous research has introduced search constraints and statistical metrics to discover significant patterns and reduce processing time. We introduce cube pairs (comparing cube groups based on a parametric statistical test) and rule pairs (based on two similar association rules), which ...
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ژورنال
عنوان ژورنال: Information Systems
سال: 2007
ISSN: 0306-4379
DOI: 10.1016/j.is.2005.05.005