The derivation problem of summary data
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
A universal-scheme approach to statistical databases containing homogeneous summary tables
ACM Transactions on Database Systems (TODS)
Answering heterogeneous database queries with degrees of uncertainty
Distributed and Parallel Databases
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Machine Learning - Special issue on context sensitivity and concept drift
IPCAT '97 Proceedings of the second international workshop on Information processing in cell and tissues
Optimal and efficient integration of heterogeneous summary tables in a distributed database
Data & Knowledge Engineering
A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Generality-Based Conceptual Clustering with Probabilistic Concepts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
IEEE Transactions on Knowledge and Data Engineering
Evaluating Aggregate Operations Over Imprecise Data
IEEE Transactions on Knowledge and Data Engineering
An Evidential Reasoning Approach to Attribute Value Conflict Resolution in Database Integration
IEEE Transactions on Knowledge and Data Engineering
Aggregation of Imprecise and Uncertain Information in Databases
IEEE Transactions on Knowledge and Data Engineering
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Learning stable concepts in a changing world
PRICAI '96 Selected Papers from the Workshop on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations: Learning and Reasoning with Complex Representations
Iterative optimization and simplification of hierarchical clusterings
Journal of Artificial Intelligence Research
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We consider the problem of characterisation of sequences of heterogeneous symbolic data that arise from a common underlying temporal pattern. The data, which are subject to imprecision and uncertainty, are heterogeneous with respect to classification schemes, where the class values differ between sequences. However, because the sequences relate to the same underlying concept, the mappings between values, which are not known ab initio, may be learned. Such mappings relate local ontologies, in the form of classification schemes, to a global ontology (the underlying pattern). On the basis of these mappings we use maximum likelihood techniques to handle uncertainty in the data and learn local probabilistic concepts represented by individual temporal instances of the sequences. These local concepts are then combined, thus enabling us to learn the overall temporal probabilistic concept that describes the underlying pattern. Such an approach provides an intuitive way of describing the temporal pattern while allowing us to take account of inherent uncertainty using probabilistic semantics.