Han, J., Nishio, S., & Kawano, W. (1994). Knowledge discovery in objectoriented and active databases. In F. Fuchi, & T. Yokoi (Eds.), Knowledge building and knowledge sharing (pp. 221--230). Singapore: IOS Press.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....using a set of knowledge discovery methods, originated from our own research, including attribute oriented induction [3] progressive deepening for mining multiple level rules [6] meta rule guided knowledge mining [2] etc. 2. The study of knowledge discovery in different kinds of databases [14, 12, 17, 13, 20], including knowledge discovery in relational, object oriented, deductive, spatial, and active databases, and global information sys tems, and the application of knowledge discovery for intelligent query answering [11] multiple layered database construction [9] etc. Research is partially ....

.... 3 Knowledge discovery in advanced database systems and knowledge discovery appli cations Beside knowledge discovery in large relational databases, investigations have also been performed on efficient and effective methods for knowledge discovery in object oriented databases [12], spatial databases [13, 14, 17] active databases [12] deductive databases [2] transaction databases [6] and global information systems [20] Three of them are outlined below to convey the ideas. For knowledge discovery in object oriented databases [12] techniques have been studied on ....

[Article contains additional citation context not shown here]

J. Hah, S. Nishio, and H. Kawano. Knowledge discovery in object-oriented and active databases. In F. Fuchi and T. Yokoi, editors, Knowledge Building and Knowledge Sharing, pages 221 230. Ohmsha, Ltd. and IOS Press, 1994.

....SAM, like R trees ca be used to make OO database more efficient in access and retreival. Therefore, exploiting OO technology in data mining is an area with enormous potential. Techniques for generalizations of complex data objects, methods and class hierarchies has been studied by Han et al. [28]. Mining under uncertainty: Use of evidential reasoning should be explored in the mining process for image databases and other databases where uncertainty modelling has to be done. As mentioned in Bell et al. s [7] evidential theory can model uncertainty better that traditional probabilistic ....

J. Han, S. Nishio, and H. Kawano. Knowledge Discovery in Object-Oriented and Active Databases. In F. Fuchi and T. Yokoi (eds), Knowledge Building and Knowledge Sharing, Ohmsha/IOS Press, 1994.

....however the type of data they examine is quite different from those used in [19] Moreover, both papers consider metaqueries solely, and do not specify a complete architecture for data mining. Knowledge discovery in object oriented databases (OODBs) is examined by Han, Nisho, and Kwanono in [7]. Their focus is mainly on the ways of discovering knowledge contained in an arbitrary OODB, rather than using an object oriented approach to organize the concept hierarchies for focusing the mining of interesting and strong association rules from large relational databases. 7 Conclusion and ....

J. Han, S. Nishio, and H. Kawan. Knowledge discovery in object-oriented and active databases. In Proceedings of the 1993 International Conference on Building and Sharing of Very Large Scale Knowledge Bases, pages 205--214, Toyko, Japan, December 1993.

.... database systems [7, 10] are popular and influential in advanced database applications, it is important to extend our domain of study from relational database systems to object oriented database systems and investigate the mechanisms for knowledge discovery in object oriented databases (00DBs) [43, 29]. Object oriented data models and systems [5, 6, 31, 33] embody rich data structures and semantics in the construction of complex databases, such as complex data objects, class subclass hierarchies, class composition hierarchies, property inheritance, methods and active data, etc. This not only ....

....a small number of generalized objects which can be summarized as a concise, generalized rule in high level terms. 4. 1 An object cube model An object based attribute oriented induction techniques has been developed in our previous studies of knowledge discovery in object oriented databases [43, 29], whose general idea is outlined above. Here we introduce an object based data cube (or briefly object cube) model which provides a flexible induction technique for mining knowledge in object oriented databases. Then we study the class generalization in object cube model. A popular conceptual ....

J. Han, S. Nishio, and H. Kawano. Knowledge discovery in object-oriented and active databases. In F. Fuchi and T. Yokoi, editors, Knowledge Building and Knowledge Sharing, pages 221-230. Ohmsha, Ltd. and IOS Press, 1994.

....six basic learning strategies can be summarized into the following generalization algorithm which extracts generalized characteristic, stable and evolution rules from a large volume of data using sampling technique. The algorithm is an extension of the basic attribute oriented induction algorithm [3, 4, 10] for learning rules in dynamic environment. Algorithm: Attribute oriented induction with random sampling in a dynamic environment Discovery of a set of generalized characteristic, stable and evolution rules in a dynamic environment based on a user s learning request. Input: i) A large volume ....

J. Han, S. Nishio, and H. Kawano, "Knowledge Discovery in Object-Oriented and Active Databases", Proc. of International Conference on Building and Sharing of Very Large-Scale Knowldge Bases'93, pp.205-214, Dec. 1993.

....like R trees can be used to make OO database more efficient in access and retrieval of data. Therefore, exploiting OO technology in data mining is an area with enormous potential. Techniques for generalizations of complex data objects, methods and class hierarchies have been studied by Han et al. [28]. Mining under uncertainty: Use of evidential reasoning [21] can be explored in the mining process for image databases and other databases where uncertainty modeling has to be done. As mentioned in Bell et al. s [7] evidential theory can model uncertainty better than traditional probabilistic ....

J. Han, S. Nishio, and H. Kawano. Knowledge Discovery in Object-Oriented and Active Databases. In F. Fuchi and T. Yokoi (eds), Knowledge Building and Knowledge Sharing, Ohmsha/IOS Press, pp. 221--230, 1994.

....substitute an SQLlike data mining language. 3. It is reasonably easy to design a data mining language for data mining in relational databases. It is a great challenge to design languages for knowledge mining in other kinds of databases, such as transaction databases [1] object oriented databases [10], spatial databases [13] multimedia databases, legacy databases, global information systems [22] etc. With the emerging activities for data mining in these databases, the design of data mining languages for such mining tasks may become an important issue in future research. ....

J. Han, S. Nishio, and H. Kawano. Knowledge discovery in object-oriented and active databases. In F. Fuchi and T. Yokoi, editors, Knowledge Building and Knowledge Sharing, pages 221--230. Ohmsha, Ltd. and IOS Press, 1994.

.... database systems [7, 10] are popular and influential in advanced database applications, it is important to extend our domain of study from relational database systems to object oriented database systems and investigate the mechanisms for knowledge discovery in object oriented databases (OODBs) [43, 29]. Object oriented data models and systems [5, 6, 31, 33] embody rich data structures and semantics in the construction of complex databases, such as complex data objects, class subclass hierarchies, class composition hierarchies, property inheritance, methods and active data, etc. This not only ....

....a small number of generalized objects which can be summarized as a concise, generalized rule in high level terms. 4. 1 An object cube model An object based attribute oriented induction techniques has been developed in our previous studies of knowledge discovery in object oriented databases [43, 29], whose general idea is outlined above. Here we introduce an object based data cube (or briefly object cube) model which provides a flexible induction technique for mining knowledge in object oriented databases. Then we study the class generalization in object cube model. A popular conceptual ....

J. Han, S. Nishio, and H. Kawano. Knowledge discovery in object-oriented and active databases. In F. Fuchi and T. Yokoi, editors, Knowledge Building and Knowledge Sharing, pages 221--230. Ohmsha, Ltd. and IOS Press, 1994.

....Man Sas Ontario Quebec Maritime NB NS NFL Saskatchewan Saskatchewan (a) b) Figure 16.1 The given and refined concept hierarchies for the attribute province . not be best suited for a particular learning task, which therefore often needs to be dynamically refined for desired learning results (Han and Fu 1994). Example 16.2 The given concept hierarchy for province (Figure 16.1 (a) based on the geographic and administrative regions of Canada, may not reflect the characteristics of research grant distribution of Computer Science in Canada. Such a hierarchy needs to be dynamically refined based on the ....

....the query, attribute threshold (the desired number of higher level attribute values) and data distribution. The refinement is performed by identifying and promoting big nodes and grouping the small ones while maximally preserving the original shape of the hierarchy (thus the semantic meaning) (Han and Fu 1994). The refined hierarchy for the query of Example 16.1 is described in Figure 16.1 (b) 2 Different concept hierarchies can be constructed on the same attribute based on different viewpoints or preferences. For example, the birthplace can be organized according to administrative regions, ....

[Article contains additional citation context not shown here]

Han, J., Nishio, S., and Kawano, H. 1994. Knowledge discovery in object-oriented and active databases. In Knowledge Building and Knowledge Sharing, ed. F. Fuchi and T. Yokoi, 221--230. Ohmsha, Ltd. and IOS Press.

....mining characteristic rules and association rules at multiple concept levels in relational databases and transaction databases. Similar methodologies can also be applied to mining various kinds of rules in advanced and or application oriented database systems, including object oriented databases [9], spatial databases [11] etc. Besides efficient mining algorithms, several other related issues need to be examine as well. 5.1 Interactive mining of multiple level rules It is essential to promote interactions of users with a knowledge discovery system during database mining, especially when ....

J. Han, S. Nishio, and H. Kawano. Knowledge discovery in object-oriented and active databases. In F. Fuchi and T. Yokoi, editors, Knowledge Building and Knowledge Sharing, pp. 221--230. Ohmsha, Ltd. and IOS Press, 1994.

....rules using a set of knowledge discovery methods, originated from our own research, including attribute oriented induction [3] progressive deepening for mining multiple level rules [6] meta rule guided knowledge mining [2] etc. 2. The study of knowledge discovery in different kinds of databases [14, 12, 17, 13, 20], including knowledge discovery in relational, object oriented, deductive, spatial, and active databases, and global information systems, and the application of knowledge discovery for intelligent query answering [11] multiple layered database construction [9] etc. Research is partially ....

.... 3 Knowledge discovery in advanced database systems and knowledge discovery applications Beside knowledge discovery in large relational databases, investigations have also been performed on efficient and effective methods for knowledge discovery in object oriented databases [12], spatial databases [13, 14, 17] active databases [12] deductive databases [2] transaction databases [6] and global information systems [20] Three of them are outlined below to convey the ideas. ffl For knowledge discovery in object oriented databases [12] techniques have been studied on ....

[Article contains additional citation context not shown here]

J. Han, S. Nishio, and H. Kawano. Knowledge discovery in object-oriented and active databases. In F. Fuchi and T. Yokoi, editors, Knowledge Building and Knowledge Sharing, pages 221--230. Ohmsha, Ltd. and IOS Press, 1994.

.... and be updated incrementally upon database updates [37] The approach has been implemented in a data mining system, DBMiner, and been experimented in several large relational databases [40, 42] The approach can also be extended to generalization based data mining in object oriented databases [41], spatial databases [53, 56] and other kinds of databases. The approach is designed for generalization based data mining. It is not suitable for mining specific patterns at primitive concept levels although it may help guiding such data mining by first finding some traces at high concept levels ....

J. Han, S. Nishio, and H. Kawano. Knowledge discovery in object-oriented and active databases. In F. Fuchi and T. Yokoi, editors, Knowledge Building and Knowledge Sharing, pages 221--230. Ohmsha, Ltd. and IOS Press, 1994.

No context found.

Han, J., Nishio, S., & Kawano, W. (1994). Knowledge discovery in objectoriented and active databases. In F. Fuchi, & T. Yokoi (Eds.), Knowledge building and knowledge sharing (pp. 221--230). Singapore: IOS Press.

No context found.

J. Han, S. Nishio, and H. Kawano. Knowledge discovery in object-oriented and active databases. In F. Fuchi and T. Yokoi, editors, Knowledge Building and Knowledge Sharing, pages 221--230. Ohmsha, Ltd. and IOS Press, 1994.

No context found.

Han, J., Nishio, S., and Kawano, H., (1994), "Knowledge Discovery in Object-Oriented and Active Databases", Knowledge Building and Knowledge Sharing, (eds) F. Fuchi and T. Yokoi, IOS Press, pp. 221230.

No context found.

J. Han, S. Nishio, and H. Kawano. Knowledge discovery in object-oriented and active databases. In F. Fuchi and T. Yokoi, editors, Knowledge Building and Knowledge Sharing, pages 221 230. Ohmsha, Ltd. and IOS Press, 1994.

No context found.

J. Han, S. Nishio and H.Kawano, "Knowledge Discovery in Object-Oriented and Active Databases" Proceedings of 22nd ACM SIGMOD International Conference on Management of Data, May 1993.

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