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Knowledge Discovery in Databases: An Attribute-Oriented Approach
, 1992
"... Knowledge discovery in databases, or data mining, is an important issue in the development of data- and knowledge-base systems. An attribute-oriented induction method has been developed for knowledge discovery in databases. The method integrates a machine learning paradigm, especially learning-from- ..."
Abstract
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Cited by 171 (15 self)
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Knowledge discovery in databases, or data mining, is an important issue in the development of data- and knowledge-base systems. An attribute-oriented induction method has been developed for knowledge discovery in databases. The method integrates a machine learning paradigm, especially learning-from-examples techniques, with set-oriented database operations and extracts generalized data from actual data in databases. An attribute-oriented concept tree ascension technique is applied in generalization, which substantially reduces the computational complexity of database learning processes. Different kinds of knowledge rules, including characteristic rules, discrimination rules, quantitative rules, and data evolution regularities can be discovered efficiently using the attribute-oriented approach. In addition to learning in relational databases, the approach can be applied to knowledge discovery in nested relational and deductive databases. Learning can also be performed with databases containing noisy data and exceptional cases using database statistics. Furthermore, the rules discovered can be used to query database knowledge, answer cooperative queries and facilitate semantic query optimization. Based upon these principles, a prototyped database learning system, DBLEARN, has been constructed for experimentation.
Discovery of General Knowledge in Large Spatial Databases
, 1993
"... Extraction of interesting and general knowledge from large spatial databases is an important task in the development of spatial data- and knowledge-base systems. In this paper, we investigate knowledge discovery in spatial databases and develop a generalization-based knowledge discovery mechanism ..."
Abstract
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Cited by 41 (4 self)
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Extraction of interesting and general knowledge from large spatial databases is an important task in the development of spatial data- and knowledge-base systems. In this paper, we investigate knowledge discovery in spatial databases and develop a generalization-based knowledge discovery mechanism which integrates attribute-oriented induction on nonspatial data and spatial merge and generalization on spatial data. The study shows that knowledge discovery has wide applications in spatial databases, and relatively efficient algorithms can be developed for discovery of general knowledge in large spatial databases.
On the unknown attribute values in learning from examples
- in Proceedings of Methodologies for Intelligent Systems
, 1991
"... Abstract. In machine learning many real-life applications data are characterized by attributes with unknown values. This paper shows that the existing approaches to learning from such examples are not sufficient. A new method is suggested, which transforms the original decision table with unknown va ..."
Abstract
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Cited by 25 (7 self)
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Abstract. In machine learning many real-life applications data are characterized by attributes with unknown values. This paper shows that the existing approaches to learning from such examples are not sufficient. A new method is suggested, which transforms the original decision table with unknown values into a new decision table in which every attribute value is known. Such a new table, in general, is inconsistent. This problem is solved by a technique of learning from inconsistent examples, based on rough set theory. Thus, two sets of rules: certain and possible are induced. Certain rules are categorical, while possible rules are supported by existing data, although conflicting data may exist as well. The presented approach may be combined with any other approach to uncertainty when processing of possible rules is concerned. 1.
Learning from Imperfect Data
- IN MACHINE LEARNING, META-REASONING AND LOGICS, P. BRAZDIL AND K.KONOLIGE (EDS
, 1990
"... Systems interacting with real-world data must address the issues raised by the possible presence of errors in the observations it makes. In this paper we first present a framework for discussing imperfect data and the resulting problems it may cause. We ..."
Abstract
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Cited by 6 (2 self)
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Systems interacting with real-world data must address the issues raised by the possible presence of errors in the observations it makes. In this paper we first present a framework for discussing imperfect data and the resulting problems it may cause. We
Discovery of Data Evolution Regularities in Large Databases
- Journal of Computer and Software Engineering
, 1994
"... . A large volume of concrete data may change over time in a database. It is important to catch the general trend of such changes and find data evolution (changing) regularities in databases in many applications. Because of the large volume of data, data evolution regularity cannot be simply expresse ..."
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Cited by 3 (0 self)
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. A large volume of concrete data may change over time in a database. It is important to catch the general trend of such changes and find data evolution (changing) regularities in databases in many applications. Because of the large volume of data, data evolution regularity cannot be simply expressed by enumeration of actual data. Machine learning technology should be adopted to extract such regularities in databases. This paper describes an attribute-oriented induction technique for discovery of data evolution regularities in relational databases. The technique extracts characteristic rules, discriminant rules, and the trends of data evolution in an evolving database, where a characteristic rule summarizes the characteristics of a set of evolving data, a discriminant rule distinguishes the general properties of a set of evolving data from a set of contrasting data, and the third one summarizes the general trend of data evolution over a period of time. Also, knowledge discovery can be ...
DYNAMIC AND CONTEXT-AWARE PROCESS ADAPTATION
"... Abstract. This Chapter re-examines the principles that underpin business process technologies to derive a novel approach that moves beyond the traditional assembly-line metaphor. Using a set of principles derived from Activity Theory, a system has been implemented, using a Service Oriented Architect ..."
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Abstract. This Chapter re-examines the principles that underpin business process technologies to derive a novel approach that moves beyond the traditional assembly-line metaphor. Using a set of principles derived from Activity Theory, a system has been implemented, using a Service Oriented Architecture, that provides support for dynamic and extensible flexibility, evolution and exception handling in business processes, based on accepted ideas of how people actually perform their work tasks. The resulting system, called the Worklet Service, makes available all of the benefits offered by Process Aware Information Systems to a wider range of organisational environments. 1.
Certificate of Acceptance
, 2007
"... workflow flexibility, adaptive workflow, service oriented ..."
and
"... Extraction of interesting and general knowledge from large spatial databases is an important task in the development of spatial data- and knowledge-base systems. In this paper, we investigate knowledge discovery in spatial databases and develop a generalization-based knowledge discovery mechanism wh ..."
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Extraction of interesting and general knowledge from large spatial databases is an important task in the development of spatial data- and knowledge-base systems. In this paper, we investigate knowledge discovery in spatial databases and develop a generalization-based knowledge discovery mechanism which integrates attribute-oriented induction on nonspatial data and spatial merge and generalization on spatial data. The study shows that knowledge discovery has wide applications in spatial databases, and relatively efficient algorithms can be developed for discovery of general knowledge in large spatial databases. 1.
IEEE TRANSACTIONS ON KN(IWLEDGE AND DATA ENGINEERING, VOL. 5, NO. 1, FEBRUARY 1993 29 Data-Driven Discovery of Quantitative
- IEEE Trans. Knowledge and Data Engineering
, 1993
"... A quantitative rule is a rule associated with quantitative information which assesses the representativehess of the rule in the database. In this paper, an efficient induction method is developed for learuing quantitative rules in relational databases. With the assistance of knowledge about concept ..."
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A quantitative rule is a rule associated with quantitative information which assesses the representativehess of the rule in the database. In this paper, an efficient induction method is developed for learuing quantitative rules in relational databases. With the assistance of knowledge about concept hierarchies, data relevance, and expected rule forms, attribute-oriented induction can be performed on the database, which integrates database operations with the learuing process and provides a simple, efficient way of learning quantitative rules from large databases. Our method learns both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learuing in the presence of noise. Moreover, learuing qualitative rules can be treated as a special case of learuing quantitative rules. Our paper shows that attributeoriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases.
Knowledge Discovery in Databases: An Attribute-Oriented Approach
"... Knowledge discovery in databases, or data mining, is an important issue in the development of data- and knowledge-base systems. An attribute-oriented induction method has been developed for knowledge discovery in databases. The method integrates a machine learning paradigm, especially learning-from- ..."
Abstract
- Add to MetaCart
(Show Context)
Knowledge discovery in databases, or data mining, is an important issue in the development of data- and knowledge-base systems. An attribute-oriented induction method has been developed for knowledge discovery in databases. The method integrates a machine learning paradigm, especially learning-from-examples techniques, with set-oriented database operations and extracts generalized data from actual data in databases. An attribute-oriented concept tree ascension technique is applied in generalization, which substantially reduces the computational complexity of database learning processes. Different kinds of knowledge rules, including characteristic rules, discrimination rules, quantitative rules, and data evolution regularities can be discovered efficiently using the attribute-oriented approach. In addition to learning in relational databases, the approach can be applied to knowledge discovery in nested relational and deductive databases. Learning can also be performed with databases containing noisy data and exceptional cases using database statistics. Furthermore, the rules discovered can be used to query database knowledge, answer cooperative queries and facilitate semantic query optimization. Based upon these principles, a prototyped database learning system, DBLEARN, has been constructed for experimentation. 1.
