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Multi-way set enumeration in weight tensors
- Source :
- Machine Learning
- Publication Year :
- 2010
- Publisher :
- Springer Science and Business Media LLC, 2010.
-
Abstract
- The analysis of n-ary relations receives attention in many different fields, for instance biology, web mining, and social studies. In the basic setting, there are n sets of instances, and each observation associates n instances, one from each set. A common approach to explore these n-way data is the search for n-set patterns, the n-way equivalent of itemsets. More precisely, an n-set pattern consists of specific subsets of the n instance sets such that all possible associations between the corresponding instances are observed in the data. In contrast, traditional itemset mining approaches consider only two-way data, namely items versus transactions. The n-set patterns provide a higher-level view of the data, revealing associative relationships between groups of instances. Here, we generalize this approach in two respects. First, we tolerate missing observations to a certain degree, that means we are also interested in n-sets where most (although not all) of the possible associations have been recorded in the data. Second, we take association weights into account. In fact, we propose a method to enumerate all n-sets that satisfy a minimum threshold with respect to the average association weight. Technically, we solve the enumeration task using a reverse search strategy, which allows for effective pruning of the search space. In addition, our algorithm provides a ranking of the solutions and can consider further constraints. We show experimental results on artificial and real-world datasets from different domains.
- Subjects :
- Theoretical computer science
business.industry
Association (object-oriented programming)
Graph theory
Machine learning
computer.software_genre
Set (abstract data type)
Ranking
Web mining
Artificial Intelligence
Enumeration
Artificial intelligence
Pruning (decision trees)
business
computer
Software
Associative property
Mathematics
Subjects
Details
- ISSN :
- 15730565 and 08856125
- Volume :
- 82
- Database :
- OpenAIRE
- Journal :
- Machine Learning
- Accession number :
- edsair.doi.dedup.....9359ddee1d06b59f34f18bf11dfb3705