T.M. Anwar, H.W. Beck, and S.B. Navathe. Knowledge mining by imprecise querying: A classification-based approach. In of the 8th International Conference on Data Engineering, pages 622--630, 1992.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....a hybrid algorithm, called AprioriHybrid. Experiments show that the AprioriHybrid has excellent 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 ....

Tarek M. Anwar, Howard W. Beck, and ShamkantB.Navathe. Knowledge mining by imprecise querying: A classification-based approach. In IEEE 8th Int'l Conf. on Data Engineering, Phoenix, Arizona, February 1992.

.... of the concept C with respect to the knowledge base K, i.e. in symbols K = C(a) DL systems has been used for building a variety of applications including (see [14] systems supporting software management [13] browsing and querying of networked information sources [16] knowledge mining [4], data archaeology [9] planning [35] learning [22] natural language understanding [7] clinical information system [18] digital libraries [36] software configuration management system [39] and web source integration [20] DLs are considered as to be attractive logics in knowledge based ....

T.M. Anwar, H.W. Beck, and S.B. Navathe. Knowledge mining by imprecise querying: A classification-based approach. In Proceedings of the 8th International Conference on Data Engineering, pages 622--630, 1992.

....facilities, conference planners may want to view hotels based on the various types of facilities. Alternatively, as a user navigates a travel database and collects hotels of interest, the system may develop a characterization of these hotels using data mining and classification techniques [ABN92, AIS93] This classification can then be used to develop a new hierarchy specifically for this user. ffl New users and users unfamiliar with the application, should not need to have any knowledge of available hierarchies. Hence, the system must be able to suggest hierarchies that are appropriate ....

T. M. Anwar, H. W. Beck, and S. B. Navathe. Knowledge Mining by Imprecise Querying: A Classification-Based Approach. In Proc. of the Int'l Conf. on Data Engineering, pages 622--630, Tempe, AZ, February 1992.

....vector type data models are too low level to appropriately capture the rich semantics found in most business data. We believe there is a need to use semantically rich data models, e.g. the entity relationship model [EN94] to model the concepts. Some initial efforts in this direction include [ABN92, KKS94, LHLQ95] Next, we need the ability to visualize database concepts like schemas, queries, indices, etc. in addition to the data. This will enhance the understanding of an analyst about the underlying database, thus aiding in the mining process. 2.3 Our Contributions The first contribution ....

T. M. Anwar, H. W. Beck, and S. B. Navathe. Knowledge mining by imprecise querying: A classification-based approach. In Proc. of the 8th Int'l Conf. on Data Engg., pages 622--630, 1992.

....the state of the data archaeologist s knowledge as s he explores the database and discovers useful facts. In this paper, we discuss some technical issues that arise in using Description logics (DLs) also known as the KLONE family of languages [18] as a front end for data archaelogy (see, e.g. [4, 2]) We begin with a brief background description of DL s, and then recapitulate the architecture used to interface DL s to information servers (from [5] Two interesting issues arise in this use of description logics: translating DL expressions to database queries, and safety of DL expressions, ....

....databases in use, we seek a general way of interfacing DL s with databases. 3 A Generic Interface Architecture We recapitulate here, from [5] the general architecture for integrating a DL knowledge base management system (DLMS) with a database management system (DBMS) see 1 See Anwar et al. [2] for a more detailed description of this use of description logics. KB DB DLMS DBMS DB DL translator Description Language Query Tranlator Organize class descriptions Incremental Classification of New Descriptions Integrate Individuals Definitions and Queries Retrieved Instances Descriptions ....

Anwar, T., Beck, H., and Navathe, S., Knowledge Mining by Imprecise Querying: A Classification Based Approach, Proceedings of the Eighth International Conference on Data Engineering.

.... mining and knowledge discovery, but also relates to a wide range of other research areas including multivariate statistics (principal component analysis, cluster analysis, and multidimensional scaling [2] database interfaces (cooperative database interfaces [3] interfaces for imprecise querying [4], intelligent data browsing [5] and information retrieval (approximate matching algorithms [6] 7] The work done in data mining focuses on the semi automatic extraction of knowledge. In all mentioned areas, important advances have been made over the last years. Many novel data mining ....

T. M. Anwar, H. W. Beck, S. B. Navathe: `Knowledge Mining by Imprecise Querying: A Classification-Based Approach', Proc. 8th Int. Conf. on Data Engineering, Tempe, AZ, pp. 622-630, 1992.

....the same transaction. Also, mining classification is an approach of trying to develop rules to group data tuples together based J. S. Park is partially supported by KOSEF, Korea. on certain common features. This has been explored both in the AI domain [12, 13] and in the context of databases [2, 6, 8]. Another source of data mining is on ordered data, such as stock market and point of sales data. Interesting aspects to explore from these ordered data include searching for similar sequences [1, 14] e.g. stocks with similar movement in stock prices, and sequential patterns [5] e.g. grocery ....

T.M. Anwar, H.W. Beck, and S.B. Navathe. Knowledge Mining by Imprecise Querying: A Classification-Based Approach. Proceedings of the 8th International Conference on Data Engineering, pages 622--630, February 1992.

....Database technology 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 ....

Tarek M. Anwar, Howard W. Beck, and Shamkant B. Navathe, "Knowledge Mining by Imprecise Querying: A Classification-Based Approach", IEEE 8th Int'l Conf. on Data Engineering, Phoenix, Arizona, Feb. 1992.

....it is therefore reasonable to consider generalizing it slightly until a non empty answer set is obtained. The infinite lattice of possible descriptions provides the obvious space to search for such generalizations, and therefore the system can provide a helping hand in this task, as illustrated in [Anwar et al. 1992]. Most modern database management systems, provide a facility for giving names to some queries, because users frequently refer to them (e.g. they represent some subset of the data or some reorganization of it) These named queries are called views in the database world. Such queries may even be ....

.... above advantages, KBMS based on description logics, such as classic [Borgida et al. 1989] loom [MacGregor 1987] and back [von Luck et al. 1987] are emerging as useful practical tools in a variety of situations, including interfaces and exploration of traditional databases [Beck et al. 1989, Anwar et al. 1992, Brachman et al. 1992] configuration management [Owsnicki Klewe 1988] and software informatio n systems [Devanbu 1991, Mark et al. 1992] Acknowledgments: I am deeply grateful to Ron Brachman for giving me the opportunity to join him in the Classic adventure and for his collaboration in many ....

Anwar, T.M., Beck H. and Navathe S., "Knowledge Mining by imprecise querying: a classification-based approach", Proc. 8th Conference on Data Engineering', Tempe, Arizona, February 1992, 622--630.

....a transaction will imply the presence of other items in the same transaction. Also, mining classification is an approach of trying to develop rules to group data tuples together based on certain common features. This has been explored both in the AI domain [10, 11] and in the context of databases [4, 6]. In general, data mining is a very application dependent issue and different applications explored will require different mining techniques to cope with. Notice, however, that in several data mining applications, the problem domain could only be vaguely defined, and hence a mathematical ....

T.M. Anwar, H.W. Beck, and S.B. Navathe. Knowledge Mining by Imprecise Querying: A Classification-Based Approach. Proceedings of the 8th International Conference on Data Engineering, pages 622--630, February 1992.

....[11] and there are various other aspects of data mining explored in the literature. Classification is an approach of trying to develop rules to group data tuples together based on certain common characteristics. This has been explored both in the AI domain [12] and in the context of databases [2, 6, 7, 10]. Another source of data mining is on ordered data, such as stock market and point of sales data. Interesting aspects to explore include searching for similar sequences [1] e.g. stocks with similar movement in stock prices, and sequential patterns [4] e.g. grocery items bought over a set of ....

T.M. Anwar, H.W. Beck, and S.B. Navathe. Knowledge Mining by Imprecise Querying: A Classification-Based Approach. Proceedings of the 8th International Conference on Data Engineering, February 1992.

....[15] and there are various other aspects of data mining explored in the literature. Classification is an approach of trying to develop rules to group data tuples together based on certain common characteristics. This has been explored both in the AI domain [16] and in the context of databases [2, 7, 8]. Mining in spatial databases was conducted in [14] Another source of data mining is on 2 A comprehensive study on various algorithms to determine large itemsets is presented in [5] where the Apriori algorithm is shown to provide the best performance during the initial iterations. Hence, ....

T.M. Anwar, H.W. Beck, and S.B. Navathe. Knowledge Mining by Imprecise Querying: A Classification-Based Approach. Proceedings of the 8th International Conference on Data Engineering, pages 622--630, February 1992.

....in many applications where access to data is required. We summarize some of these applications of DLs, though for a fuller survey the reader is directed to [11] The present work is directly motivated by the recent use of DLs as query languages to access data stored in relational databases [16, 2, 23]. There has been considerable research on the complexity of reasoning with descriptions, especially computing the subsumption (containment) relationship between them. Surprisingly little is known about their expressive power as query languages, in comparison with well known formalisms based on ....

....queries and existential queries. For these languages, we gain ideas for subsumption algorithms from the work in the database literature on the containment problem of existential queries. A brief consideration of two DLs that have appeared in the database literature (classic [12, 16] and candide [7, 2]) indicates that they can express queries that cannot be stated in Datalog, but cannot express certain first order queries. Finally, we indicate how one can transfer to DLs results from the theoretical database literature about languages based on predicate calculus with recursion. 2 Descriptions ....

[Article contains additional citation context not shown here]

Anwar, T.M., Beck H. and Navathe S., "Knowledge Mining by imprecise querying: a classification-based approach", Proc. 8th IEEE Data Engineering Conf., Tempe, AZ, February 1992, 622--630.

....a hybrid algorithm, called AprioriHybrid. Experiments show that the AprioriHybrid has excellent 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 ....

Tarek M. Anwar, Howard W. Beck, and Shamkant B. Navathe. Knowledge mining by imprecise querying: A classification-based approach. In IEEE 8th Int'l Conf. on Data Engineering, Phoenix, Arizona, February 1992.

....This contrasts with IMACS, where both the notion of a hypothesis is much richer, and analysis follows more of top down flow as the initial, and quite detailed, domain model gets investigated and elaborated through segmentations captured in the knowledge representation. Anwar, Navathe and Beck [5, 6] describe an approach to knowledge discovery where new classes are formed over a set of existing instances in multiple relational databases. The scheme uses both the multiple relational schemas and the data instances to integrate them into a single knowledge representation (using the CANDIDE data ....

Anwar, T. M., Beck, H. W., and Navathe, S. B., Knowledge Mining by Imprecise Querying: A Classification-Based Approach, Proceedings of the 8th Int'l Conference on Data Engineering, Tempe, Arizona, Feb. 3-7, 1992, pp. 622-630.

....cases, it is reasonable to consider generalizing the query slightly until a non empty answer set is obtained. The lattice of subsuming descriptions provides the obvious space to search for such generalizations, and therefore the system can provide a helping hand in this task, as illustrated in [3]. ffl The description lattice supports the paradigm of query specification by iterative refinement, described in [67] and [57] ffl Data exploration involves asking very many queries, possibly by teams of people, over an extended period of time. The DL based KBMS can automatically organize ....

T.W. Anwar, H. Beck and S. Navathe, "Knowledge mining by imprecise querying: a classification-based approach", Proc. 8th Conference on Data Engineering', Tempe, Arizona, February 1992, 622--630.

....supported in part by 95 SPECIAL FUND for UNIVERSITY RESEARCH INSTITUE, Korea Research Foundation. 1. Introduction In scientific database and decision support database, the importance of ad hoc queries is still increasing[Ioan94] But some database users do not understand the whole database schema[Anwe92][Ioan92] The reason is that database application area expands and database schema is becoming more and more complex. Database users are likely to be confused whether an attribute of a class are related or not. Here is a sample query (cf. Fig. 1 for the corresponding database schema) What are ....

Tarek M Anwar, Howard W. Beck, Knowledge Mining by Imprecise Querying : a Classification-based Approach, Proc. 1992 ICDE, pp 622-630, 1992.

....a transaction will imply the presence of other items in the same transaction. Also, mining classification is an approach of trying to develop rules to group data tuples together based on certain common features. This has been explored both in the AI domain [16, 17] and in the context of databases [2, 6, 12]. Mining in spatial databases was conducted in [14] Another source of data mining is on ordered data, such as stock market and point of sales data. Interesting aspects to explore from these ordered data include searching for similar sequences [1, 19] e.g. stocks with similar movement in stock ....

T.M. Anwar, H.W. Beck, and S.B. Navathe. Knowledge Mining by Imprecise Querying: A Classification-Based Approach. Proceedings of the 8th International Conference on Data Engineering, pages 622--630, February 1992.

.... analysis techniques are classical statistical models [26] Methods have also been studied for scaling machine learning algorithms by combining base classifiers from partitioned data sets [18] There have also been some studies of classification techniques in the context of large databases [2, 10]. An interval classifier has been proposed in [2] to reduce the cost of decision tree generation. The neural network approach for classification and rule extraction in databases has also been studied recently [55] 5.2 Methods for performance improvement Most of the techniques developed in machine ....

T.M. Anwar, H.W. Beck, and S.B. Navathe. Knowledge Mining by Imprecise Querying: A Classification-Based Approach. Proceedings of the 8th International Conference on Data Engineering, pages 622--630, February 1992.

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T.M. Anwar, H.W. Beck, and S.B. Navathe. Knowledge mining by imprecise querying: A classification-based approach. In of the 8th International Conference on Data Engineering, pages 622--630, 1992.

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Anwar T. M., Beck H. W., Navathe S. B.: `Knowledge Mining by Imprecise Querying: A Classification-Based Approach', Proc. 8th Int. Conf. on Data Engineering, Tempe, AZ, 1992, pp. 622-630.

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Anwar, T. M., Beck, H., and Navathe, S., "Knowledge mining by imprecise querying: A classification-based approach," Proc. 8th IEEE Data Engineering Conf., Tempe, AZ, February, 1992, pp. 622--630.

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T.W. Anwar, H. Beck and S. Navathe, "Knowledge mining by imprecise querying: a classification-based approach", Proc. 8th Conference on Data Engineering', Tempe, Arizona, February 1992, 622--630.

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Anwar TW, Beck H and Navathe S, "Knowledge mining by imprecise querying: a classification based approach" Proc 8th Conf. on Data Engineering, Tempe, Arizona, USA, Feb 1992 pp: 622-630.

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