The category concept: an extension to the entity-relationship model
Data & Knowledge Engineering
Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Learning improved integrity constraints and schemas from exceptions in databases and knowledge bases
On knowledge base management systems: integrating artificial intelligence and d atabase technologies
Artificial intelligence (2nd ed.)
Artificial intelligence (2nd ed.)
Computational complexity of terminological reasoning in BACK
Artificial Intelligence
Semantic database modeling: survey, applications, and research issues
ACM Computing Surveys (CSUR)
Object flavor evolution in an object-oriented database system
COCS '88 Proceedings of the ACM SIGOIS and IEEECS TC-OA 1988 conference on Office information systems
A learning-based approach to meta-data evolution in an object-oriented database
Lecture notes in computer science on Advances in object-oriented database systems
CLASSIC: a structural data model for objects
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Incorporating hierarchy in a relational model of data
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Explanation-based learning: a problem solving perspective
Artificial Intelligence
Conceptual clustering of explanations
Proceedings of the sixth international workshop on Machine learning
Conceptual clustering of mean-ends plans
Proceedings of the sixth international workshop on Machine learning
Natural language, cognitive models, and simulation
Qualitative simulation modeling and analysis
The simulation of verbal learning behavior
Computers & thought
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Conceptual Clustering, Categorization, and Polymorphy
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Classification as a Query Processing Technique in the CANDIDE Semantic Data Model
Proceedings of the Fifth International Conference on Data Engineering
Knowledge Mining by Imprecise Querying: A Classification-Based Approach
Proceedings of the Eighth International Conference on Data Engineering
Loading data into description reasoners
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A database design for uniform representation of hypermedia and mathematical models
WSC '94 Proceedings of the 26th conference on Winter simulation
Conceptual schema analysis: techniques and applications
ACM Transactions on Database Systems (TODS)
Description Logics in Data Management
IEEE Transactions on Knowledge and Data Engineering
Unsupervised Learning with Mixed Numeric and Nominal Data
IEEE Transactions on Knowledge and Data Engineering
Building Classes in Object-Based Languages by Automatic Clustering
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
A population approach to ubicomp system design
Proceedings of the 2010 ACM-BCS Visions of Computer Science Conference
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Conceptual clustering techniques based on current theories of categorization provide a way to design database schemas that more accurately represent classes. An approach is presented in which classes are treated as complex clusters of concepts rather than as simple predicates. An important service provided by the database is determining whether a particular instance is a member of a class. A conceptual clustering algorithm based on theories of categorization aids in building classes by grouping related instances and developing class descriptions. The resulting database schema addresses a number of properties of categories, including default values and prototypes, analogical reasoning, exception handling, and family resemblance. Class cohesion results from trying to resolve conflicts between building generalized class descriptions and accommodating members of the class that deviate from these descriptions. This is achieved by combining techniques from machine learning, specifically explanation-based learning and case-based reasoning. A subsumption function is used to compare two class descriptions. A realization function is used to determine whether an instance meets an existing class description. A new function, INTERSECT, is introduced to compare the similarity of two instances. INTERSECT is used in defining an exception condition. Exception handling results in schema modification. This approach is applied to the database problems of schema integration, schema generation, query processing, and view creation.