D.H. Lee, M.H. Kim, "Database summarization using fuzzy ISA hierarchies", IEEE Transactions on Systems, Man, and Cybernetics - part B, 27(1), 1997, pp. 68-78. Home/Search Document Details and Download
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Data Mining in Soft Computing Framework: A Survey - Mitra, Pal, Fellow, Mitra (2001) (7 citations) (Correct)
....are natural groupings of data items based on similarity metrics or probability density models. 4) Rule generation [34] 41] extracts classification rules from the data. 5) Discovering association rules [42] 45] describes association relationship among different attributes. 6) Summarization [46] [49] provides a compact description for a subset of data. 7) Dependency modeling [50] 51] describes significant dependencies among variables. 8) Sequence analysis [52] 53] models sequential patterns, like time series analysis. The goal is to model the states of the process generating ....
....4) Data Summarization: Summary discovery is one of the major components of knowledge discovery in databases. This provides the user with comprehensive information for grasping the essence from a large amount of information in a database. Fuzzy set theory is also used for data summarization [46]. Typically, fuzzy sets are used for an interactive top down summary discovery process which utilizes fuzzy IS A hierarchies as domain knowledge. Here generalized tuples are used as a representational form of a database summary including fuzzy concepts. By virtue of fuzzy IS A hierarchies, where ....
D. H. Lee and M. H. Kim, "Database summarization using fuzzy ISA hierarchies," IEEE Trans. Syst., Man, Cybern. B, vol. 27, pp. 68--78, 1997.
Mining Fuzzy Rules in a Donor Database for Direct Marketing.. - Chan, Au, Choi (Correct)
....summaries introduced in [17] express knowledge in linguistic representation, which is natural for human users to comprehend. In addition to linguistic summaries, an interactive top down summary discovery process, which utilizes fuzzy is a hierarchies as domain knowledge, has been described in [11]. This technique aims at discovering a set of generalized tuples. Unlike association rules, which involve implications between different attributes, linguistic summaries and generalized tuples provide summarization on different attributes only. The idea of implication has not been taken into ....
D.H. Lee and M.H. Kim, "Database Summarization Using Fuzzy ISA Hierarchies," IEEE Trans. on Systems, Man, and Cybernetics -- Part B: Cybernetics, vol. 27, no. 4, pp. 671-680, 1997.
FARM: A Data Mining System for Discovering Fuzzy Association Rules - Au, Chan (1999) (1 citation) (Correct)
....natural for people to comprehend. An example of linguistic summaries is the statement about half of people are middle aged. In addition to fuzzy linguistic summaries, an interactive top down summary discovery process which utilizes fuzzy is a hierarchies as domain knowledge has been described in [9]. This technique aims at discovering a set of generalized tuples such as technical writer, documentation . In contrast to association rules which involve implications between different attributes, fuzzy linguistic summaries and the generalized tuples only provide summarization on different ....
D.H. Lee and M.H. Kim, "Database Summarization Using Fuzzy ISA Hierarchies," IEEE Trans. on Systems, Man, and Cybernetics -- Part B: Cybernetics, vol. 27, no. 4, pp. 671-680, Aug. 1997.
Intelligent Techniques for Handling Uncertainty in the.. - Garibaldi (1997) (Correct)
....in the past [55, 84] Recently, these methods of crisp rule induction have been extended into the domain of fuzzy theory. Fuzzy rule induction is a process whereby a large quantity of real example data is searched for patterns and correlations in order to provide fuzzy rules of interpretation [10, 71]. Fuzzy rule induction combined with expert knowledge elicitation may allow the formulation the rules required for the other modules. To be useful, an expert system must produce output in a similar form to that produced by experts in the domain. In this domain, as in most medical expert systems, ....
D.H. Lee and Kim M.H. Database summarization using fuzzy ISA hierarchies. IEEE Transactions Systems Man Cybernetics, 27(4B):671--680, 1997.
Data Mining in Soft Computing Framework: A Survey - Mitra, Pal, Mitra (2001) (7 citations) (Correct)
....metrics or probability density models. 4. Rule generation [34] 35] 36] 37] 38] 39] 40] 41] extracts classification rules from the data. 5. Discovering association rules [42] 43] 44] 45] describes association relationship among di#erent attributes. 6. Summarization [46], 47] 48] 49] provides a compact description for a subset of data. 7. Dependency modeling [50] 51] describes significant dependencies among variables. 8. Sequence analysis [52] 53] models sequential patterns, like time series analysis. The goal is to model the states of the process ....
....A.4 Data summarization Summary discovery is one of the major components of knowledge discovery in databases. This provides the user with comprehensive information for grasping the essence from a large amount of information in a database. Fuzzy set theory is also used for data summarization [46]. Typically, fuzzy sets are used for an interactive top down summary discovery process which utilizes fuzzy IS A hierarchies as domain knowledge. Here generalized tuples are used as a representational form of a database summary including fuzzy concepts. By virtue of fuzzy IS A hierarchies, where ....
D. H. Lee and M. H. Kim, "Database summarization using fuzzy ISA hierarchies," IEEE Transactions on Systems Man and Cybernetics. Part B-Cybernetics, vol. 27, pp. 68--78, 1997.
Mining Multi-Level Associations with Fuzzy Hierarchies - Angryk, Petry (2005) (Correct)
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D.H. Lee, M.H. Kim, "Database summarization using fuzzy ISA hierarchies", IEEE Transactions on Systems, Man, and Cybernetics - part B, 27(1), 1997, pp. 68-78.
Knowledge Discovery in Fuzzy Databases Using - Rafal (Correct)
No context found.
Lee DH & Kim MH (1997) Database summarization using fuzzy ISA hierarchies. IEEE Transactions on Systems, Man, and Cybernetics - part B 27(1), pp. 68-78.
Copyright 2005, Idea Group Inc. Copying or distributing in print.. - Idea (Correct)
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Lee, D., & Kim, M. (1997). Database summarization using fuzzy ISA hierarchies. IEEE Transactions On Sysems, Man, and Cybernetics --- Part B, 27(1), 68--78.
Prototyping and Browsing Image Databases Using.. - Saint-Paul, Raschia.. (2002) (Correct)
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D. H. Lee and M. H. Kim. Database summarization using fuzzy isa hierarchies. Ieee Trans. On Systems Man & Cybernetics-Part B: Cybernetics, 27:68--78, feb 1997.
Image Database Summarization with the SaintEtiQ System - Saint-Paul, Raschia, Mouaddib (2002) (Correct)
No context found.
D. H. Lee and M. H. Kim. Database summarization using fuzzy isa hierarchies. Ieee Trans. On Systems Man & Cybernetics-Part B: Cybernetics, 27:68-- 78, feb 1997.
Intelligent Techniques for Handling Uncertainty in the.. - Garibaldi (1997) (Correct)
No context found.
D.H. Lee and Kim M.H. Database summarization using fuzzy ISA hierarchies. IEEE Transactions Systems Man Cybernetics, 27(4B):671--680, 1997.
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