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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 1995年第5期7卷 691-698页

作者： WUTHRICH, B Hong Kong University of Science and Technology Hong Kong China

We define a new fixpoint semantics for rule-based reasoning in the presence of weighted information. The semantics is illustrated on a real-world application requiring such reasoning. Optimizations and approximations of the semantics are shown so as to make the semantics amenable to very large scale real-world applications, We finally prove that the semantics is probabilistic and reduces to the usual fixpoint semantics of stratified Datalog if all information is certain. We implemented various knowledge discovery systems which automatically generate such probabilistic decision rules. In collaboration with a bank in Hong Kong we use one such system to forecast currency exchange rates.

关键词： AXIOMATIC PROBABILITY THEORY DATA MINING INCOMPLETE INFORMATION knowledge discovery in databases QUERY OPTIMIZATION AND APPROXIMATION STRATIFIED DATALOG

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Formally analysing the concepts of domestic violence

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第4期38卷 3116-3130页

作者： Poelmans, Jonas Elzinga, Paul Viaene, Stijn Dedene, Guido Katholieke Univ Leuven Fac Business & Econ B-3000 Louvain Belgium Vlerick Leuven Gent Management Sch B-3000 Louvain Belgium Amsterdam Amstelland Police NL-1000 CG Amsterdam Netherlands Univ Amsterdam Sch Business NL-1018 WB Amsterdam Netherlands

The types of police inquiries performed these days are incredibly diverse. Often data processing architectures are not suited to cope with this diversity since most of the case data is still stored as unstructured text. In this paper Formal Concept Analysis (FCA) is showcased for its exploratory data analysis capabilities in discovering domestic violence intelligence from a dataset of unstructured police reports filed with the Amsterdam-Amstelland police in the Netherlands. From this data analysis it is shown that FCA can be a powerful instrument to operationally improve policing practice. For one, it is shown that the definition of domestic violence employed by the police is not always as clear as it should be, making it hard to use it effectively for classification purposes. In addition, this paper presents newly discovered knowledge for automatically classifying certain cases as either domestic or non-domestic violence. Moreover, it provides practical advice for detecting incorrect classifications performed by police officers. A final aspect to be discussed is the problems encountered because of the sometimes unstructured way of working of police officers. The added value of this paper resides in both using FCA for exploratory data analysis, as well as with the application of FCA for the detection of domestic violence. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Formal Concept Analysis (FCA) Domestic violence knowledge discovery in databases Text mining Exploratory data analysis knowledge enrichment Concept discovery

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Discovering partial periodic-frequent patterns in a transactional database

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JOURNAL OF SYSTEMS AND SOFTWARE 2017年 125卷 170-182页

作者： Kiran, R. Uday Venkatesh, J. N. Toyoda, Masashi Kitsuregawa, Masaru Reddy, P. Krishna Univ Tokyo Tokyo Japan B Natl Inst Commun Technol Tokyo Japan Natl Inst Informat Tokyo Japan Int Inst Informat Technol Hyderabad Hyderabad Andhra Pradesh India

Time and frequency are two important dimensions to determine the interestingness of a pattern in a database. Periodic-frequent patterns are an important class of regularities that exist in a database with respect to these two dimensions. Current studies on periodic-frequent pattern mining have focused on discovering full periodic-frequent patterns, i.e., finding all frequent patterns that have exhibited complete cyclic repetitions in a database. However, partial periodic-frequent patterns are more common due to the imperfect nature of real-world. This paper proposes a flexible and generic model to find partial periodic frequent patterns. A new interesting measure, periodic-ratio, has been introduced to determine the periodic interestingness of a frequent pattern by taking into account its proportion of cyclic repetitions in a database. The proposed patterns do not satisfy the anti-monotonic property. A novel pruning technique has been introduced to reduce the search space effectively. A pattern-growth algorithm to find all partial periodic-frequent patterns has also been presented in this paper. Experimental results demonstrate that the proposed model can discover useful information, and the algorithm is efficient. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Data mining knowledge discovery in databases Pattern mining Partial periodicity Algorithms

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PYTHIA-II: A knowledge/database system for managing performance data and recommending scientific software

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ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE 2000年第2期26卷 227-253页

作者： Houstis, EN Catlin, AC Rice, JR Verykios, VS Ramakrishnan, N Houstis, CE Purdue Univ Dept Comp Sci W Lafayette IN 47906 USA Drexel Univ Coll Informat Sci & Technol Philadelphia PA 19104 USA Virginia Polytech Inst & State Univ Dept Comp Sci Blacksburg VA 24061 USA Univ Crete Dept Comp Sci Iraklion Greece

Often scientists need to locate appropriate software for their problems and then select from among many alternatives. We have previously proposed an approach for dealing with this task by processing performance data of the targeted software. This approach has been tested using a customized implementation referred to as PYTHIA. This experience made us realize the complexity of the algorithmic discovery of knowledge from performance data and of the management of these data together with the discovered knowledge. To address this issue, we created PYTHLA-II-a modular framework and system which combines a general knowledge discovery in databases (KDD) methodology and recommender system technologies to provide advice about scientific software/hardware artifacts. The functionality and effectiveness of the system is demonstrated for two existing performance studies using sets of software for solving partial differential equations. From the end-user perspective, PYTHIA-II allows users to specify the problem to be solved and their computational objectives. In turn, PYTHIA-II (i) selects the software available for the user's problem, (ii) suggests parameter values, and (iii) assesses the recommendation provided. PYTHIA-II provides all the necessary facilities to set up database schemas for testing suites and associated performance data in order to test sets of software. Moreover, it allows easy interfacing of alternative data mining and recommendation facilities. PYTHIA-II is an open-ended system implemented on public domain software and has been used for performance evaluation in several different problem domains.

关键词： algorithms experimentation data mining inductive logic programming knowledge-based systems knowledge discovery in databases performance evaluation recommender systems scientific software

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Application of temporal descriptors to musical instrument sound recognition

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2003年第1期21卷 71-93页

作者： Wieczorkowska, AA Wróblewski, J Synak, P Slezak, D Polish Japanese Inst Informat Technol PL-02008 Warsaw Poland Univ Regina Dept Comp Sci Regina SK S4S 0A2 Canada

An automatic content extraction from multimedia files is recently being extensively explored. However, an automatic content description of musical sounds has not been broadly investigated and still needs an intensive research. In this paper, we investigate how to optimize sound representation in terms of musical instrument recognition purposes. We propose to trace trends in the evolution of values of MPEG-7 descriptors in time, as well as their combinations. Described process is a typical example of KDD application, consisting of data preparation, feature extraction and decision model construction. Discussion of efficiency of applied classifiers illustrates capabilities of possible progress in the optimization of sound representation. We believe that further research in this area would provide background for an automatic multimedia content description.

关键词： knowledge discovery in databases music content processing multimedia content description MPEG-7

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Identification of high-risk beneficiaries in private healthcare insurance

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HEALTH INFORMATICS JOURNAL 2024年第1期30卷

作者： Santos, Adauto Leal, Gislaine Camila Lapasini Balancieri, Renato Univ Estadual Maringa Technol Ctr Maringa Brazil State Univ Parana Comp Sci Collegiate Apucarana Brazil Univ Estadual Maringa Technol Ctr Av Colombo 5790Zona 7 BR-87020900 Maringa Brazil

The objective of this study was to apply the knowledge discovery in databases process to find out if beneficiaries of a private healthcare insurance would belong, at least once, to the 'very high cost' and 'complex cases' groups throughout the 12 months after the month when algorithms were applied. Datasets were built containing information on beneficiaries' effective use of their health plan, as well as their characteristics. Five machine learning algorithms were used, namely Random forest, Extra tree, Xgboost, Naive bayes and K-nearest neighbor. The K-nearest neighbor algorithm had a recall rate of 81.12%, 83.77% precision and an Area Under the Curve (AUC) value of 0.9045. The study also revealed that categorization occurs, on average, 8.11 months before a beneficiary entering, for the first time, a high-risk group, considering the dataset classification from January 2019 to June 2020.

关键词： data mining healthcare health insurance knowledge discovery in databases machine learning

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Some Massively Parallel Algorithms from Nature

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Wuhan University Journal of Natural Sciences 2002年第1期7卷 37-46页

作者： Li Yan, Kang Li-shan, Chen Yu-ping, Liu Pu, Cao Hong-qing, Pan Zheng-jun The State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, China The State Key Laboratory of Software Engineering Wuhan University Wuhan Hubei China

We introduced the work on parallel problem solvers from physics and biology being developed by the research team at the State Key Laboratory of Software Engineering, Wuhan University. Results on parallel solvers include the following areas: Evolutionary algorithms based on imitating the evolution processes of nature for parallel problem solving, especially for parallel optimization and model-building; Asynchronous parallel algorithms based on domain decomposition which are inspired by physical analogies such as elastic relaxation process and annealing process, for scientific computations, especially for solving nonlinear mathematical physics problems. All these algorithms have the following common characteristics: inherent parallelism, self-adaptation and self-organization, because the basic ideas of these solvers are from imitating the natural evolutionary processes.

关键词： evolutionary computation parallel algorithm imitating nature domain decomposition knowledge discovery in databases

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Efficient discovery of correlated patterns using multiple minimum all-confidence thresholds

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2015年第3期45卷 357-377页

作者： Rage, Uday Kiran Kitsuregawa, Masaru Univ Tokyo Inst Ind Sci Tokyo Japan Natl Inst Informat Tokyo Japan

Correlated patterns are an important class of regularities that exist in a database. Although there exists no universally acceptable best measure to judge the interestingness of a pattern, all-confidence is emerging as a popular measure to discover the patterns. It is because the measure satisfies both the anti-monotonic and null-invariance properties. The former property makes the pattern mining practicable in real-world applications. The latter property facilitates the user to discover the patterns involving both frequent and rare items without generating the huge number of patterns. In this paper, we show that though the measure satisfies the null-invariance property, mining the patterns containing both frequent and rare items with a single minimum all-confidence (minAllConf) threshold leads to the dilemma known as "rare item problem." At a high minAllConf, the discovered correlated patterns involving rare items have very short length. At a low minAllConf, combinatorial explosion can occur, producing too many patterns. To confront the problem, the paper introduces an alternative model based on the concept of multiple minAllConf thresholds. The proposed model generalizes the existing model of correlated patterns and facilitates the user to specify a different minAllConf for each pattern depending upon its items' frequencies. A pattern-growth algorithm, called GCoMine, has also been proposed to discover the patterns. Experiment results show that GCoMine is efficient, and the proposed model can address the problem effectively.

关键词： Data mining knowledge discovery in databases Correlated patterns Rare item problem

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Fractal-Based Intrinsic Dimension Estimation and Its Application in Dimensionality Reduction

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2012年第1期24卷 59-71页

作者： Mo, Dengyao Huang, Samuel H. Univ Cincinnati Intelligent Syst Lab Dept Mech Engn Cincinnati OH 45221 USA

Dimensionality reduction is an important step in knowledge discovery in databases. Intrinsic dimension indicates the number of variables necessary to describe a data set. Two methods, box-counting dimension and correlation dimension, are commonly used for intrinsic dimension estimation. However, the robustness of these two methods has not been rigorously studied. This paper demonstrates that correlation dimension is more robust with respect to data sample size. In addition, instead of using a user selected distance d, we propose a new approach to capture all log-log pairs of a data set to more precisely estimate the correlation dimension. Systematic experiments are conducted to study factors that influence the computation of correlation dimension, including sample size, the number of redundant variables, and the portion of log-log plot used for calculation. Experiments on real-world data sets confirm the effectiveness of intrinsic dimension estimation with our improved method. Furthermore, a new supervised dimensionality reduction method based on intrinsic dimension estimation was introduced and validated.

关键词： Intrinsic dimension fractal dimension feature selection knowledge discovery in databases

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Discovering interesting inclusion dependencies: application to logical database tuning

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INFORMATION SYSTEMS 2002年第1期27卷 1-19页

作者： Lopes, S Petit, JM Toumani, F Univ Clermont Ferrand Lab LIMOS FRE 2239 CNRS F-63177 Aubiere France

Inclusion dependencies together with functional dependencies form the most important data dependencies used in practice. Inclusion dependencies are important for various database applications such as database design and maintenance, semantic query optimization and efficient view maintenance of data warehouse. Existing approaches for discovering inclusion dependencies consist in producing the whole set of inclusion dependencies holding in a database, leaving the task of selecting the interesting ones to an expert user. In this paper, we take another look at the problem of discovering inclusion dependencies. We exploit the logical navigation, inherently available in relational databases through workloads of SQL statements, as a guess to automatically find out only interesting inclusion dependencies. This assumption leads us to devise a tractable algorithm for discovering interesting inclusion dependencies. Within this framework, approximate dependencies, i.e. inclusion dependencies which almost hold, are also considered. As an example, we present a novel application, namely self-tuning the logical database design, where the discovered inclusion dependencies can be used effectively. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： relational model inclusion dependencies knowledge discovery in databases data mining self-tuning databases

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