OpenRaiser/Intern-Atlas huggingface dataset:
Using Approximations to Scale Exploratory Data Analysis in Datacubes
Authors: Daniel Barbará, Xintao Wu
Venue: KDD
Year: 1999

Exploratory Data Analysis is a widely used technique to determine which factors have the most influence on data values in a multi-way table, or which cells in the table can be considered anomalous with respect to the other cells. In particular, median polish is a simple, yet robust method to perform Exploratory Data Analysis. Median polish is resistant to holes in the table (cells that have no values), but it may require a lot of iterations through the data. This factor makes it difficult to apply median polish to large multidimensional tables, since the I/O requirements may be prohibitive. This paper describes a technique that uses median polish over an approximation of a datacube, easing the burden of I/O. The results obtained are tested for quality, using a variety of measures. The technique scales to large datacubes and proves to give a good approximation of the results that would have been obtained by median polish in the original data. 1 Introduction Exploratory Data...
Density-Based Indexing for Approximate Nearest-Neighbor Queries
Authors: Kristin P. Bennett, Usama M. Fayyad, Dan Geiger
Venue: KDD
Year: 1999

We consider the problem of performing Nearest-neighbor queries efficiently over large high-dimensional databases. To avoid a full database scan, we target constructing a multidimensional index structure. It is well-accepted that traditional database indexing algorithms fail for high-dimensional data (say d > 10 or 20 depending on the scheme). Some arguments have advocated that nearest-neighbor queries do not even make sense for high-dimensional data. We show that these arguments are based on over-restrictive assumptions, and that in the general case it is meaningful and possible to build an index for such queries. Our approach, called DBIN, scales to high-dimensional databases by exploiting statistical properties of the data. The approach is based on statistically modeling the density of the content of the data table. DBIN uses the density model to derive a single index over the data table and requires physically rewriting data in a new table sorted by the newly created index (i.e. create a clustered-index).
A Classification-Based Methodology for Planning Audit Strategies in Fraud Detection
Authors: Francesco Bonchi, Fosca Giannotti, Gianni Mainetto, Dino Pedreschi
Venue: KDD
Year: 1999

Planning adequate audit strategies is a key success factor in a posterion' fraud detection, e.g., in the fiscal and insurance domains, where audits are intended to detect tax evasion and fraudulent claims. A case study is presented in this paper, which illustrates how techniques based on classification can be used to support the task of planning audit strategies. The proposed approach is sensible to some conflicting issues of audit planning, e.g., the trade-off between maximizing audit benefits vs. minimizing audit costs. A methodological scenario, common to a whole class of similar applications, is then abstracted away from the case study. The limitations of available systems to support the identified overall KDD process, bring us to point out the key aspects of a logic-based database language, integrated with mining mechanisms, which is used to provide a uniform, highly expressive environment for the various steps in the construction of the considered case-study.
Mining Optimized Gain Rules for Numeric Attributes
Authors: Sergey Brin, Rajeev Rastogi, Kyuseok Shim
Venue: KDD
Year: 1999

Abstract—Association rules are useful for determining correlations between attributes of a relation and have applications in the marketing, financial, and retail sectors. Furthermore, optimized association rules are an effective way to focus on the most interesting characteristics involving certain attributes. Optimized association rules are permitted to contain uninstantiated attributes and the problem is to determine instantiations such that either the support, confidence, or gain of the rule is maximized. In this paper, we generalize the optimized gain association rule problem by permitting rules to contain disjunctions over uninstantiated numeric attributes. Our generalized association rules enable us to extract more useful information about seasonal and local patterns involving the uninstantiated attribute. For rules containing a single numeric attribute, we present an algorithm with linear complexity for computing optimized gain rules. Furthermore, we propose a bucketing technique that can result in a significant reduction in input size by coalescing contiguous values without sacrificing optimality. We also present an approximation algorithm based on dynamic programming for two numeric attributes. Using recent results on binary space partitioning trees, we show that the approximations are within a constant factor of the optimal optimized gain rules. Our experimental results with synthetic data sets for a single numeric attribute demonstrate that our algorithm scales up linearly with the attribute’s domain size as well as the number of disjunctions. In addition, we show that applying our optimized rule framework to a population survey real-life data set enables us to discover interesting underlying correlations among the attributes.
Towards Automated Synthesis of Data Mining Programs
Authors: Wray L. Buntine, Bernd Fischer, Thomas Pressburger
Venue: KDD
Year: 1999

Code synthesis is routinely used in industry to generate GUIs, form filling applications, and database support code and is even used with COBOL. In this paper we consider the question of whether code synthesis could also be applied to the data mining phase of knowledge discovery. We view this as a rapid prototyping method. Rapid prototyping of statistical data analysis algorithms would allow experienced analysts to experiment with different statistical models before choosing one, but without requiring prohibitively expensive programming efforts. It would also smooth the steep learning curve often faced by novice users of data mining tools and libraries. Finally, it would accelerate dissemination of essential research results and the development of applications. In this paper, we present a framework and the basic software for the automated synthesis of data analysis programs. We use a specification language that generalizes Bayesian networks, a popular notation used in many communities...
Applying General Bayesian Techniques to Improve TAN Induction
Authors: Jesús Cerquides
Venue: KDD
Year: 1999

Free Augmented Naive Bayes (TAN) has shown to be competitive with state-of-the-art machine learning algorithms [3]. However, the TAN induction algorithm that appears in [3] can be improved in several ways. In this paper we identify three shortcomings in it and introduce two ideas to overcome those problems: the multinomial sampling approach to learning bayesian networks and local bayesian model averaging. These ideas approaches are generic and can thus be reused to improve other learning algorithms. We empirically test the new algorithms, and conclude that in most of the cases they lead to an improvement in accuracy in the classification and in the quality of the probabilities given as predictions.
NonStop SQL/MX Primitives for Knowledge Discovery
Authors: John Clear, Debbie Dunn, Brad Harvey, Michael L. Heytens, Peter Lohman, Abhay Mehta, Mark Melton, Lars Rohrberg, Ashok Savasere, Robert M. Wehrmeister, Melody Xu
Venue: KDD
Year: 1999

In this paper, we describe the primitives that we have implemented in NonStop SQL/MX, a commercial DBMS, to support the knowledge discovery process. These primitives, combined with the high-performance SQL&IX engine, represent a powerful kernel for performing basic knowledge discovery tasks in a scalable and efftcient manner.
Information Mining Platforms: An Infrastructure for KDD Rapid Deployment
Authors: Corinna Cortes, Daryl Pregibon
Venue: KDD
Year: 1999

Experience has shown that the data extraction, parsing, cleaning and analysis steps of a KDD problem account for a much larger expenditure of resources (time and money) than the statistical modeling or machine learning part. Couple this statement with the need for fast turn-around time in commercial applications, and the obvious conclusion is that it is impractical to start from scratch for each new KDD application.
Bayesian Networks for Lossless Dataset Compression
Authors: Scott Davies, Andrew W. Moore
Venue: KDD
Year: 1999

The recent explosion in research on probabilistic data mining algorithms such as Bayesian networks has been focussed primarily on their use in diagnostics, prediction and efficient inference.
MetaCost: A General Method for Making Classifiers Cost-Sensitive
Authors: Pedro M. Domingos
Venue: KDD
Year: 1999

Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD problems. Individually making each classification learner costsensitive is laborious, and often non-trivial. In this paper we propose a principled method for making an arbitrary classifier cost-sensitive by wrapping a cost-minimizing procedure around it. This procedure, called MetaCost, treats the underlying classifier as a black box, requiring no knowledge of its functioning or change to it. Unlike stratification, MetaCost, is applicable to any number of classes and to arbitrary cost matrices. Empirical trials on a large suite of benchmark databases show that MetaCost almost always produces large cost reductions compared to the cost-blind classifier used (C4.5RULES) and to two forms of stratification. Further tests identify the key components of MetaCost and those that can be varied without substantial loss. Experiments on a larger database indicate that MetaCost scales well.
Squashing Flat Files Flatter
Authors: William DuMouchel, Chris Volinsky, Theodore Johnson, Corinna Cortes, Daryl Pregibon
Venue: KDD
Year: 1999

A feature of data mining that distinguishes it from &quot;classical&quot; machine learning (ML) and statistical modeling (SM) is scale. The community seems to agree on this yet progress to this point has been limited. We present a methodology that addresses scale in a novel fashion that has the potential for revolutionizing the field. While the methodology applies most directly to flat (row by column) data sets we believe that it can be adapted to other representations. Our approach to the problem is not to scale up individual ML and SM methods. Rather we prefer to leverage the entire collection of existing methods by scaling down the data set. We call the method squashing. Our method demonstrably outperforms random sampling and a theoretical argument suggests how and why it works well. Squashing consists of three modular steps: grouping, momentizing, and generating (GMG). These three steps describe the squashing pipeline whereby the original (very large data set) is sectioned off into mutual...
Combining Estimators to Improve Performance
Authors: John F. Elder IV, Greg Ridgeway
Venue: KDD
Year: 1999

Despite the diverse pedigrees of Data Mining methods, the underlying algorithms fall into a handful of families, whose properties suggest their likely performance on a given dataset. One typically selects an algorithm by matching its strengths to the properties of one's data. Yet, performance surprises, where competing models rank differently than expected, are common; model inference, even when semi-automated, seems to yet be as much art as science.
Mining Unstructured Data
Authors: Ronen Feldman
Venue: KDD
Year: 1999

Article Free Access Share on Mining unstructured data Author: Ronen Feldman Mathematics and Computer Science Department of Bar-Ilan University in Israel Mathematics and Computer Science Department of Bar-Ilan University in IsraelView Profile Authors Info & Claims KDD '99: Tutorial notes of the fifth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 1999 Pages 182–236https://doi.org/10.1145/312179.312192Online:01 August 1999Publication History 5citation1,519DownloadsMetricsTotal Citations5Total Downloads1,519Last 12 Months85Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Classification and Regression: Money *can* Grow on Trees
Authors: Johannes Gehrke, Wei-Yin Loh, Raghu Ramakrishnan
Venue: KDD
Year: 1999

With over 800 million pages covering most areas of human endeavor, the World-wide Web is a fertile ground for data mining research to make a difference to the effectiveness of information search. Today, Web surfers access the Web through two dominant interfaces clicking on hyperlinks and searching via keyword queries This process is often tentative and unsatisfactory Better support is needed for expressing one's information need and dealing with a search result in more structured ways than available now. Data mining and machine learning have significant roles to play towards this end.
Event Detection from Time Series Data
Authors: Valery Guralnik, Jaideep Srivastava
Venue: KDD
Year: 1999

Article Event detection from time series data Share on Authors: Valery Guralnik Department of Computer Science, University of Minnesota Department of Computer Science, University of MinnesotaView Profile , Jaideep Srivastava Department of Computer Science, University of Minnesota Department of Computer Science, University of MinnesotaView Profile Authors Info & Claims KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 1999 Pages 33–42https://doi.org/10.1145/312129.312190Online:01 August 1999Publication History 284citation6,313DownloadsMetricsTotal Citations284Total Downloads6,313Last 12 Months264Last 6 weeks21 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Farming the Web for Systematic Business Intelligence (Invited talk, Abstract only)
Authors: Richard D. Hackathorn
Venue: KDD
Year: 1999

The technologies of data warehousing, data mining, hypertext analysis, information visualization, and web information resources are rapidly converging. The challenge is to architect these technologies into a system for systematic business intelligence for a corporation. We need to move from an information refining process that is often haphazard and narrow to one that is reliable and continuous. Web farming is a new area that suggests a methodology and architecture for accomplishing this.
Data Mining by Business Users: Integrating Data Mining in Business Processes
Authors: Marcel Holsheimer
Venue: KDD
Year: 1999

Data mining is often performed on an ad-hoc basis where fairly complex business problems are analyzed. However, we believe that the greater value of data mining is not in such complex analysis by statisticians or data mining experts, but in a day to day use of data mining as part of a company's primary processes. These so-called data mining solutions will be used by business users, such as marketeers, rather than by statisticians. Data Distilleries has gained considerable experience in implementing data mining solutions, based on the European KESO research framework.
WAPS, a Data Mining Support Environment for the Planning of Warranty and Goodwill Costs in the Automobile Industry
Authors: Edgar Hotz, Gholamreza Nakhaeizadeh, B. Petzsche, H. Spiegelberger
Venue: KDD
Year: 1999

Article Free Access Share on WAPS, a data mining support environment for the planning of warranty and goodwill costs in the automobile industry Authors: E. Hotz DaimlerChrysler AG, Research & Technology 3, P.O.Box 2360, 89013 Ulm DaimlerChrysler AG, Research & Technology 3, P.O.Box 2360, 89013 UlmView Profile , G. Nakhaeizadeh DaimlerChrysler AG, Research & Technology 3, P.O.Box 2360, 89013 Ulm DaimlerChrysler AG, Research & Technology 3, P.O.Box 2360, 89013 UlmView Profile , B. Petzsche DaimlerChtysler AG, Sales and Services Direction, Stuttgart, 70546 Stuttgart DaimlerChtysler AG, Sales and Services Direction, Stuttgart, 70546 StuttgartView Profile , H. Spiegelberger DaimlerChtysler AG, Sales and Services Direction, Stuttgart, 70546 Stuttgart DaimlerChtysler AG, Sales and Services Direction, Stuttgart, 70546 StuttgartView Profile Authors Info & Claims KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 1999 Pages 417–419https://doi.org/10.1145/312129.312307Published:01 August 1999Publication History 6citation556DownloadsMetricsTotal Citations6Total Downloads556Last 12 Months18Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Adaptive Query Processing for Time-Series Data
Authors: Yun-Wu Huang, Philip S. Yu
Venue: KDD
Year: 1999

Traditional query process for time-series data transforms data from the time domain into the frequency domain. It is less controllable by the end users, therefore isn't well suited for queries that find patterns with many dynamically specified user constraints. For these queries we present a method to search timeseries data which first transforms time sequences into symbol strings using change ratio between contiguous data points in time series. Next, a suffix tree is built to index all suffixes of the symbol strings. The focus of this paper is to demonstrate how this method can adapt to the processing of the dynamically constrained time-series queries.
Optimization of Collection Efforts in Automobile Financing - a KDD Supported Environment
Authors: H. Kauderer, Gholamreza Nakhaeizadeh, F. Artiles, H. Jeromin
Venue: KDD
Year: 1999

This paper presents a project realized in the domain of risk management at a financial services subsidiary of DaimlerChrysler. The project resulted in the implementation of an innovative KDD approach designed to optimize the collection efforts in automobile financing. In contrast with the conventional "credit scoring"which is mainly geared towards assessing a customer's credit worthiness at the time when she/he applies for a loan -in this project we followed the credit life cycle downstream and focused on how to best deal with delinquent customers in an actual credit portfolio. The resulting Data Mining solution was implemented in the finance company's systems environment and in daily operations.
Clustering Techniques for Large Data Sets - from the Past to the Future
Authors: Daniel A. Keim, Alexander Hinneburg
Venue: KDD
Year: 1999

Application Example: Marketing – Given: • Large data base of customer data containing their properties and past buying records
The Impact of Changing Populations on Classifier Performance
Authors: Mark G. Kelly, David J. Hand, Niall M. Adams
Venue: KDD
Year: 1999

Article Free Access Share on The impact of changing populations on classifier performance Authors: Mark G. Kelly Department of Mathematics, Imperial College, 180 Queen's Gate, London, SW7 2BZ, UK Department of Mathematics, Imperial College, 180 Queen's Gate, London, SW7 2BZ, UKView Profile , David J. Hand Department of Mathematics, Imperial College, 180 Queen's Gate, London, SW7 2BZ, UK Department of Mathematics, Imperial College, 180 Queen's Gate, London, SW7 2BZ, UKView Profile , Niall M. Adams Department of Mathematics, Imperial College, 180 Queen's Gate, London, SW7 2BZ, UK Department of Mathematics, Imperial College, 180 Queen's Gate, London, SW7 2BZ, UKView Profile Authors Info & Claims KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 1999 Pages 367–371https://doi.org/10.1145/312129.312285Published:01 August 1999Publication History 125citation965DownloadsMetricsTotal Citations125Total Downloads965Last 12 Months110Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Mining in a Data-Flow Environment: Experience in Network Intrusion Detection
Authors: Wenke Lee, Salvatore J. Stolfo, Kui W. Mok
Venue: KDD
Year: 1999

We discuss the KDD process in "data-flow" environments, where unstructured and time dependent data can be processed into various levels of structured and semanticallyrich forms for analysis tasks.
Mining Association Rules with Multiple Minimum Supports
Authors: Bing Liu, Wynne Hsu, Yiming Ma
Venue: KDD
Year: 1999

Association rule mining is an important model in data mining. Its mining algorithms discover all item associations (or rules) in the data that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf) constraints. Minsup controls the minimum number of data cases that a rule must cover. Minconf controls the predictive strength of the rule. Since only one minsup is used for the whole database, the model implicitly assumes that all items in the data are of the same nature and/or have similar frequencies in the data. This is, however, seldom the case in reallife applications. In many applications, some items appear very frequently in the data, while others rarely appear. If minsup is set too high, those rules that involve rare items will not be found. To find rules that involve both frequent and rare items, minsup has to be set very low. This may cause combinatorial explosion because those frequent items will be associated with one another in all possible ways. T...
Mining Interesting Knowledge Using DM-II
Authors: Bing Liu, Wynne Hsu, Yiming Ma, Shu Chen
Venue: KDD
Year: 1999

1. Introduction Data mining aims to develop a new generation of tools tointelligently assist humans in analyzing mountains of data.
Origami: A New Data Visualization Tool
Authors: Jen Que Louie, Tom Kraay
Venue: KDD
Year: 1999

Article Origami: a new data visualization tool Share on Authors: Jen Que Louie Nautilus Systems, Inc., 3867 Aider Woods Court, Fairfax, VA Nautilus Systems, Inc., 3867 Aider Woods Court, Fairfax, VAView Profile , Tom Kraay Nautilus Systems, Inc., 3867 Aider Woods Court, Fairfax, VA Nautilus Systems, Inc., 3867 Aider Woods Court, Fairfax, VAView Profile Authors Info & Claims KDD '99: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 1999 Pages 405–408https://doi.org/10.1145/312129.312302Online:01 August 1999Publication History 1citation540DownloadsMetricsTotal Citations1Total Downloads540Last 12 Months4Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access