D. J. Lubinsky. Discovery from database: A review of AI and statistical techniques. In Proc. IJCAI-89 Workshop on Knowledge Discovery in Databases, pages 204-218, Detroit, MI, August 1989.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....rule relies on the selection of the attributes to be generalized and the selection of generalization operators. Such selections can be based on data semantics, user preference, generalization efficiency, etc. Many techniques developed in previous studies on machine learning [14] statistics [13], fuzzy set and rough set theories [22] etc. can be applied to the selection of attributes and operators. Interesting rules can often be discovered by following different paths leading to several generalized relations for examination, comparison and selection, which can be performed interactively ....

D. J. Lubinsky. Discovery from database: A review of AI and statistical techniques. In Proc. IJCAI-89 Workshop on Knowledge Discovery in Databases, pages 204-218, Detroit, MI, August 1989.

....scale up properties, opening up the feasibility of mining association rules over very large databases. The problem of finding association rules falls within the purview of database mining [AIS93a] ABN92] HS94] MKKR92] S 93] Tsu90] also called knowledge discovery in databases [HCC92] Lub89] PS91b] Related, but not directly applicable, work includes the induction of classification rules [BFOS84] Cat91] FWD93] HCC92] Qui93] discovery of causal rules [CH92] Pea92] learning of logical definitions [MF92] Qui90] fitting of functions to data [LSBZ87] Sch90] and clustering ....

David J. Lubinsky.Discovery from databases: A review of AI and statistical techniques. In IJCAI-89 Workshop on Knowledge Discovery in Databases, pages 204--218, Detroit, August 1989.

.... artificial neural networks, genetic algorithms, and various statistical techniques [Qui93] Classification has been studied primarily in the AI community, and the computational complexity of these algorithms generally inhibits the performance efficiency necessary when mining large databases [Lub89] furthermore, the algorithms do not scale well with increasing database size. In the database community, the work in [AGI 92, MAR96, SAM96] has focused on designing efficient classification algorithms for large databases. The goal of classification is to compute a predictive model for each ....

D. J. Lubinsky. Discovery from databases: A review of ai and statistical techniques. In IJCAI--89 Workshop on Knowledge Discovery in Databases, pages 204--218, 1989.

....a notion of justifying rules to suggest the refinements of the rules. Piatetsky Shapiro [16] has discussed the expected accuracy of the discovered rules by using a statistical function about the number of tuples. There is a significant difference between our approach and the previous research [2,3,6,9,11,20,21] The previous research has concentrated on how artificial intelligence can help in knowledge discovery, without considering the characteristics of databases. As shown in the diagonal vector (2 ) of the following diagram, logically, if new rules X are discovered from a database S (i.e. S X ) ....

D. Lubinsky. Discovery from databases: A review of ai and statistical techniques. Proc. IJCAI-89 Workshop on Knowledge Discovery in Databases, pages 204--218, 1989.

....has been used with great success in traditional business data processing. There is an increasing desire to use this technology in new application domains. One such application domain that is likely to acquire considerable significance in the near future is database mining [12] 3] 5] 8] 9] [11] [15] 16] 18] 19] An increasing number of organizations are creating ultra large data bases (measured in gigabytes and even terabytes) of business data, such as consumer data, transaction histories, sales records, etc. Such data forms a potential gold mine of valuable business information. ....

....of database mining. These classes certainly do not exhaust all database mining applications, but do capture an interesting subset of them. In Section 3, we will present a unifying framework for studying and solving these problems. 2. 1 Classification The classification problem [6] 10] [11] [18] involves finding rules that partition the given data into disjoint groups. As an example of a classification problem, consider the store location problem. It is assumed that the success of the store is determined by the neighborhood characteristics, and the company is interested in ....

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David J. Lubinsky, "Discovery from Databases: A Review of AI and Statistical Techniques", IJCAI-89 Workshop on Knowledge Discovery in Databases, Detroit, August 1989, 204--218.

....scale up properties, opening up the feasibility of mining association rules over very large databases. The problem of finding association rules falls within the purview of database mining [AIS93a] ABN92] HS94] MKKR92] S 93] Tsu90] also called knowledge discovery in databases [HCC92] Lub89] PS91b] Related, but not directly applicable, work includes the induction of classification rules [BFOS84] Cat91] FWD93] HCC92] Qui93] discovery of causal rules [CH92] Pea92] learning of logical definitions [MF92] Qui90] fitting of functions to data [LSBZ87] Sch90] and clustering ....

David J. Lubinsky. Discovery from databases: A review of AI and statistical techniques. In IJCAI-89 Workshop on Knowledge Discovery in Databases, pages 204--218, Detroit, August 1989.

....commercial database products in business data processing, the market place is showing evidence of increasing desire to use database technology in new application domains. One such application domain that is likely to acquire considerable significance in the near future is database mining [4] 10] [12] [16] 18] 17] 20] Several organizations have created ultra large data bases, running into several gigabytes and more. The databases relate to various aspects of their business and are information mines that they would like to exploit to improve the quality of their decision making. One ....

....: A 1 Theta A 2 Theta . An G j for j = 1; m. We also refer to the examples set E as the training set and the database D as the test data set . This problem has been investigated in the AI and Statistics literature under the topic of supervised learning (see, for example, 6] 11] [12] [17] 1 . We put the following additional requirements, not considered in the classical treatment of the problem, on the classification functions: 1. Retrieval Efficiency : The classification function should be able to exploit database indexes to minimize the number of redundant objects ....

[Article contains additional citation context not shown here]

David J. Lubinsky, "Discovery from Databases: A Review of AI and Statistical Techniques", AT&T Bell Laboratories, Holmdel, New Jersey, June 1989.

....rule relies on the selection of the attributes to be generalized and the selection of generalization operators. Such selections can be based on data semantics, user preference, generalization efficiency, etc. Many techniques developed in previous studies on machine learning [14] statistics [13], fuzzy set and rough set theories [22] etc. can be applied to the selection of attributes and operators. Interesting rules can often be discovered by following different paths leading to several generalized relations for examination, comparison and selection, which can be performed interactively ....

D. J. Lubinsky. Discovery from database: A review of AI and statistical techniques. In Proc. IJCAI-89 Workshop on Knowledge Discovery in Databases, pages 204--218, Detroit, MI, August 1989.

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D.J. Lubinsky, (1989). Discovery from Database: A Review of AI and Statistical Techniques, Proceedings of IJCA-89 Worshop on Knowledge Discovery in Databases, Detroit, Michigan, 204-218.

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