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Contrastive antichains in hierarchies
- Source :
- SIGKDD 2019, SIGKDD 2019, Aug 2019, Anchorage, Alaska, United States. pp.294:304, ⟨10.1145/3292500.3330954⟩, KDD, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- International audience; Concepts are often described in terms of positive integer-valued attributes that are organized in a hierarchy. For example, cities can be described in terms of how many places there are of various types (e.g. nightlife spots, residences, food venues), and these places are organized in a hierarchy (e.g. a Portuguese restaurant is a type of food venue). This hierarchy imposes particular constraints on the values of related attributes---e.g. there cannot be more Portuguese restaurants than food venues. Moreover, knowing that a city has many food venues makes it less surprising that it also has many Portuguese restaurants, and vice versa. In the present paper, we attempt to characterize such concepts in terms of so-called contrastive antichains: particular kinds of subsets of their attributes and their values. We address the question of when a contrastive antichain is interesting, in the sense that it concisely describes the unique aspects of the concept, and this while duly taking into account the known attribute dependencies implied by the hierarchy. Our approach is capable of accounting for previously identified contrastive antichains, making iterative mining possible. Besides the interestingness measure, we also present an algorithm that scales well in practice, and demonstrate the usefulness of the method in an extensive empirical results section.
- Subjects :
- Jefrey Lijffijt
Technology and Engineering
Learning in probabilistic graphical models
Computer science
[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]
02 engineering and technology
Antichains
• Computing methodologies → Knowledge representation and reasoning
Antichain
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Pattern Mining ACM Reference Format: Anes Bendimerad
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
020204 information systems
and Tijl De
0202 electrical engineering, electronic engineering, information engineering
Unsupervised learning KEYWORDS Antichains
Subjective Interestingness
Céline Robardet
Hierarchy
Measure (data warehouse)
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]
SUBGROUP DISCOVERY
Pattern Mining
Data science
Marc Plantevit
020201 artificial intelligence & image processing
CCS CONCEPTS • Mathematics of computing → Information theory
Probabilis- tic representations
Subjects
Details
- Language :
- English
- ISBN :
- 978-1-4503-6201-6
- ISBNs :
- 9781450362016
- Database :
- OpenAIRE
- Journal :
- SIGKDD 2019, SIGKDD 2019, Aug 2019, Anchorage, Alaska, United States. pp.294:304, ⟨10.1145/3292500.3330954⟩, KDD, KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING
- Accession number :
- edsair.doi.dedup.....770ff530ac2a293234910c9c7cf94f52
- Full Text :
- https://doi.org/10.1145/3292500.3330954⟩