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Using background knowledge in the aggregation of imprecise evidence in databases

DATA & knowledge ENGINEERING 2000年第2期32卷 131-143页

作者： McClean, S Scotney, B Shapcott, M Univ Ulster Sch Informat & Software Engn Fac Informat Coleraine BT52 1SA Londonderry North Ireland

Background knowledge of data is often available, arising from a concept hierarchy, as integrity constraints, from database integration, or from knowledge possessed by domain experts. Frequently databases contain incomplete data which we may represent using Dempster-Shafer mass functions to represent evidence contained in subsets of the domain. These subsets may be represented in the database as partial values which are derived from background knowledge using logic programming to re-engineer the database. For such a data model we develop an aggregation operator which calculates Dempster-Shafer mass functions and thus facilities decision making and knowledge discovery in data-bases. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： evidence theory background knowledge aggregation operator imprecise information knowledge discovery in databases

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System for managing and refining structural characteristics discovered from databases

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knowledge-BASED SYSTEMS 1996年第4期9卷 267-279页

作者： Zhong, N Ohsuga, S WASEDA UNIV DEPT INFORMAT & COMP SCI SHINJUKU KU TOKYO 169 JAPAN

Systems that allow automatic knowledge discovery from databases will play an increasingly important role in building/sharing large scale knowledge bases. Although many systems for knowledge discovery in databases have been proposed, few of them have addressed the capabilities of managing and refining the discovered knowledge. In particular, the contents of most databases are ever changing and erroneous data can be a significant problem in real-world databases. Hence, the process of discovering knowledge from databases is a process based on incipient hypothesis generation/evaluation and refinement/management. The paper describes a system named IIBR (Inheritance Inference Based Refinement) for managing and refining structural characteristics discovered from databases. Structural characteristics are a kind of important regularity hidden in databases, and are denoted by regression models for describing three kinds of functional relations: the exact, strong and weak ones. IIBR is one subsystem of the authors' GLS (Global Learning Scheme) discovery system, and can be cooperatively used with other subsystems of GLS such as KOSI (knowledge Oriented Statistic Inference). By means of IIBR, the structural characteristics discovered by KOSI can be added to a knowledge base as the deductive rules and the sets of data for showing their errors, and can be easily managed and refined according to data change in a database. IIBR is based on inheritance inference and error analysis, as well as the model representation of knowledge, multiple worlds/levels, and metareasoning in the knowledge-based system KAUS. Experience with a prototype of IIBR implemented by KAUS is discussed.

关键词： knowledge discovery in databases inheritance inference error analysis data change knowledge representation

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Extraction and applications of statistical relationships in relational databases

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 1996年第6期8卷 939-945页

作者： Hou, WC Department of Computer Science Southem Illinois University Carbondale IL USA

In this paper, we discuss modeling and extraction of statistical relationships among attributes. Different methods are used for extraction of different types of relationships. A complete methodology for extraction is developed by integrating widely accepted statistical methods. Statistical relationships manifest embedded relationships in data and thus lend themselves naturally to estimating unknown attribute values and detecting unlikely values. We will carefully examine these applications and evaluate the usefulness of statistical relationships in these applications using a real-life database.

关键词： data mining estimating unknown attribute values, integration of data mining techniques integrity constraints knowledge discovery in databases statistical relationships

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Generalization-based data mining in object-oriented databases using an object cube model

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DATA & knowledge ENGINEERING 1998年第1-2期25卷 55-97页

作者： Han, J Nishio, S Kawano, H Wang, W Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada Osaka Univ Dept Informat Syst Engn Osaka 5650871 Japan Kyoto Univ Dept Appl Syst Sci Kyoto 606850101 Japan

Data mining is the discovery of knowledge and useful information from the large amounts of data stored in databases. With the increasing popularity of object-oriented database systems in advanced database applications, it is important to study the data mining methods for object-oriented databases because mining knowledge from such databases may improve understanding, organization, and utilization of the data stored there. In this paper, issues on generalization-based data mining in object-oriented databases are investigated in three aspects: (1) generalization of complex objects, (2) class-based generalization, and (3) extraction of different kinds of rules. An object cube model is proposed for class-based generalization, on-line analytical processing, and data mining. The study shows that (i) a set of sophisticated generalization operators can be constructed for generalization of complex data objects, (ii) a dimension-based class generalization mechanism can be developed for object cube construction, and (iii) sophisticated rule formation methods can be developed for extraction of different kinds of knowledge from data, including characteristic rules, discriminant rules, association rules, and classification rules. Furthermore, the application of such discovered knowledge may substantially enhance the power and flexibility of browsing databases, organizing databases and querying data and knowledge in object-oriented databases.

关键词： data mining knowledge discovery in databases object-oriented databases object cube model

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DISCOVERING CONCEPT CLUSTERS BY DECOMPOSING databases

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DATA & knowledge ENGINEERING 1994年第2期12卷 223-244页

作者： ZHONG, N OHSUGA, S Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo 153 Japan

This paper introduces an approach of discovering concept clusters by decomposing databases. This approach is the fundamental one for developing DBI which is one of sub-systems of the GLS discovery system implemented by us. A key feature of this approach is the formation of concept clusters or sub-databases through analysis and deletion of noisy data in decomposing a database. Its development is based on the concept of Simon and Ando's near-complete decomposability that has been most explicitly used in economic theory. In this approach, the process of discovering concept clusters from databases is a process based on incipient hypothesis generation and refinement, and many kinds of learning methods, in which the methods of data-driven and knowledge-driven are included, are cooperatively used in multiple learning phases, so that a more robust, general discovery system can be developed.

关键词： knowledge discovery in databases CONCEPTUAL CLUSTERING NEAR-COMPLETE DECOMPOSABILITY MULTIPLE LEARNING PHASES INTEGRATION

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Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2011年第3期52卷 123-139页

作者： Gagliardi, Francesco Univ Roma La Sapienza Dept Philosophy I-00161 Rome Italy

Objective: The aim of this paper is to study the feasibility and the performance of some classifier systems belonging to family of instance-based (IB) learning as second-opinion diagnostic tools and as tools for the knowledge extraction phase in the process of knowledge discovery in clinical databases. Materials and methods: We consider three clinical databases: one relating to the differential diagnosis of erythemato-squamous diseases, the second to the diagnosis of the onset of diabetes mellitus and the third dealing with a problem of diagnostic imaging in nuclear cardiology. We apply five IB classifiers to each database: two are based on exemplars, one is based on prototypes and two are hybrid. One of the latter classifiers is a new classifier introduced here and is called prototype exemplar learning classifier (PEL-C). We use cross-validation techniques to evaluate and compare the performances of several classifier systems as diagnostic tools, considering indexes such as accuracy, sensitivity, specificity, and conciseness of class representations. Moreover we analyze the number and the type of instances that represent the diagnostic classes learnt by each classifier to evaluate and compare their knowledge extraction capabilities. Results: An examination of the experimental results shows that classifiers with the best classification performances are the optimized k-nearest neighbour classifier (k-NNC) and PEL-C. The k-NNC uses the highest number of representative instances, 100% of the entire database, whereas PEL-C uses a far lesser number of representative instances: equal, on the average, to the 3% of the database. As tools for knowledge extraction, we interpret the kind of class representations obtained by IB classifiers as a form of nosological knowledge. Additionally, we report the most interesting diagnostic class representations to be those extracted by PEL-C because they are composed of a mixture of abstracted prototypical cases (syndromes) and selected

关键词： Instance-based learning Nearest-neighbour rule Prototype methods Bias-variance dilemma Nosological diagnosis knowledge discovery in databases Erythemato-squamous diseases Diabetes mellitus Single proton emission computed tomography

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Mining multiple-level association rules in large databases

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 1999年第5期11卷 798-805页

作者： Han, JW Fu, WJ Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada Simon Fraser Univ Intelligent Database Syst Res Lab Burnaby BC V5A 1S6 Canada Univ Missouri Dept Comp Sci Rolla MO 65409 USA

A top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the Apriori principle. A group of variant algorithms is proposed based on the ways of sharing intermediate results, with the relative performance tested and analyzed. The enforcement of different interestingness measurements to find more interesting rules, and the relaxation of rule conditions for finding "level-crossing" association rules, are also investigated in the paper. Our study shows that efficient algorithms can be developed from large databases for the discovery of interesting and strong multiple-level association rules.

关键词： data mining knowledge discovery in databases association rules multiple-level association rules algorithms performance

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Modeling of Algorithms of Inductive Concept Formation in "Noisy" databases

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AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS 2013年第4期47卷 151-161页

作者： Vagin, V. N. Fomina, M. V. Antipov, S. G. Moscow Power Engn Inst Dept Appl Math Moscow Russia

The problem is inductive concept formation in the case of the processing of incomplete, inaccurate, and inconsistent information stored in real data sets. In order to generalize information from real databases it is proposed to use production models and decision trees. Models of noise are presented and the effect of noise on the operation of the proposed generalization algorithms is examined. The results of the program modeling are given.

关键词： knowledge discovery in databases generalization task decision tree production rules noise model training set

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Mining fatty acid databases for detection of novel compounds in aerobic bacteria

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JOURNAL OF MICROBIOLOGICAL METHODS 2006年第3期66卷 410-433页

作者： Dawyndt, Peter Vancanneyt, Marc Snauwaert, Cindy De Baets, Bernard De Meyer, Hans Swings, Jean Univ Ghent Microbiol Lab Dept Biochem Physiol & Microbiol B-9000 Ghent Belgium Univ Ghent Dept Appl Math Biometr & Proc Control B-9000 Ghent Belgium Univ Ghent BCCM LMG Bacteria Collect B-9000 Ghent Belgium Univ Ghent Dept Appl Math & Comp Sci B-9000 Ghent Belgium

This study examines how the discriminatory power of an automated bacterial whole-cell fatty acid identification system can be significantly enhanced by exploring the vast amounts of information accumulated during 15years of routine gas chromatographic analysis of the fatty acid content of aerobic bacteria. Construction of a global peak occurrence histogram based upon a large fatty acid database is shown to serve as a highly informative tool for assessing the delineation of the naming windows used during the automatic recognition of fatty acid compounds. Along the lines of this data mining application, it is suggested that several naming windows of the Sherlock MIS TSBA50 peak naming method may need to be re-evaluated in order to fit more closely with the bulk of observed fatty acid profiles. At the same time, the global peak occurrence histogram has put forward the delineation of 32 new peak naming windows, accounting for a 26% increase in the total number of fatty acid features taken into account for bacterial identification. By scrutinizing the relationships between the newly delineated naming windows and the many taxonomic units covered within a proprietary fatty acid database, all new naming windows were proven to correspond with stable features of some specific groups of microorganisms. This latter analysis clearly underscores the impact of incorporating the new fatty acid compounds for improving the resolution of the bacterial identification system and endorses the applicability of knowledge discovery in databases within the field of microbiology. (c) 2006 Elsevier B.V All rights reserved.

关键词： bacterial identification data mining fatty acid analysis feature extraction knowledge discovery in databases

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2001年第1期13卷 79-95页

作者： Braunmüller, B Ester, M Kriegel, HP Sander, J Univ Munich Inst Comp Sci D-80538 Munich Germany

Metric databases are databases where a metric distance function is defined for pairs of database objects. In such databases, similarity queries in the form of range queries or k-nearest-neighbor queries are the most important query types. In traditional query processing, single queries are issued independently by different users. In many data mining applications, however, the database is typically explored by iteratively asking similarity queries for answers of previous similarity queries. In this paper, we introduce a generic scheme for such data mining algorithms and we investigate two orthogonal approaches, reducing I/O cost as well as CPU cost, to speed-up the processing of multiple similarity queries. The proposed techniques apply to any type of similarity query and to an implementation based on an index or using a sequential scan. Parallelization yields an additional impressive speed-up. An extensive performance evaluation confirms the efficiency of our approach.

关键词： knowledge discovery in databases data mining similarity search efficient query processing high-dimensional indexing

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