OpenRaiser/Intern-Atlas huggingface dataset:
A general probabilistic framework for clustering individuals and objects
Authors: Igor V. Cadez, Scott Gaffney, Padhraic Smyth
Venue: KDD
Year: 2000

This paper presents a unifying probabilistic framework for clustering individuals or systems into groups when the available data measurements are not multivariate vectors of xed dimensionality. For example, one might have data from a set of medical patients, where for each patient one has a set of of observed time-series, each time-series of potentially dierent length and dierent sampling rate. We propose a general model-based probabilistic framework for clustering data types of this form which are non-vector in nature and may vary in size from individual to individual. The Expectation-Maximization (EM) procedure for clustering within this framework is discussed and we discuss how it be applied in a general manner to clustering of sequences, time-series, trajectories, and other non-vector data. We show that a number of earlier algorithms can be viewed as special cases within this unifying framework. The paper concludes with several illustrations of the method, including clustering o...
Discovering similar patterns in time series
Authors: Juan Pedro Caraça-Valente, Ignacio Lopez-Chavarrias
Venue: KDD
Year: 2000

In this paper, we describe the process of discovering underlying knowledge in a set of isokinetic tests, using a new algorithm to find similar patterns in a set of temporal series. An isokinetic machine is basically a physical support on which patients exercise one of their joints, in this case the knee, according to different ranges of movement and at a constant speed. The data on muscle strength supplied by the machine are processed by an expert system that has built-in knowledge elicited from an expert in isokinetics. It cleans and pre-processes the data and conducts an intelligent analysis of the parameters and morphology of the isokinetic curves. Then, Data Mining methods based on the discovery of sequential patterns in time series by means of which to find similarities and differences among exercises were applied to the processed information to characterise injuries of those patients. The results obtained were applied in two environments: one for the blind and another for elite athletes.
Incremental quantile estimation for massive tracking
Authors: Fei Chen, Diane Lambert, José C. Pinheiro
Venue: KDD
Year: 2000

Data-call records, internet packet headers, or other transaction records-are coming down a pipe at a ferocious rate, and we need to monitor statistics of the data. There is no reason to think that the data are normally distributed, so quantiles of the data are important to watch. The probe attached to the pipe has only limited memory, though, so it is impossible to compute the quantiles by sorting the data. The only possibility is to incrementally estimate the quantiles as the data fly by. This paper provides such an incremental quantile estimator. It resembles an exponentially weighted moving average in form, processing and memory requirements, but it is based on stochastic approximation so we call it an exponentially weighted stochastic approximation or EWSA. Simulations show that the EWSA outperforms other kinds of incremental estimates that also require minimal main memory, especially when extreme quantiles are tracked for patterns of behavior that change over time. Use of the EWSA is illustrated in an application to tracking call duration for a set of callers over a three month period.
Identifying prospective customers
Authors: Paul B. Chou, Edna Grossman, Dimitrios Gunopulos, Pasumarti Kamesam
Venue: KDD
Year: 2000

We describe data mining techniques designed to address the problem of selecting prospective customers from a large pool of candidates. These techniques cover a number of different scenarios, namely whether the marketing researchers have demographic information on the current customers, or the general market population, or people with propensity to become customers We also present a novel approach to the problem by exploiting the availability of a data sample from the general market population. Finally, we describe an on-line lead management and delivery system that uses the mining approach described in this paper for insurance agents to obtain qualified customer leads.
Hancock: a language for extracting signatures from data streams
Authors: Corinna Cortes, Kathleen Fisher, Daryl Pregibon, Anne Rogers
Venue: KDD
Year: 2000

Article Hancock: a language for extracting signatures from data streams Share on Authors: Corinna Cortes AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJ AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJView Profile , Kathleen Fisher AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJ AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJView Profile , Daryl Pregibon AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJ AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJView Profile , Anne Rogers AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJ AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJView Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 9–17https://doi.org/10.1145/347090.347094Online:01 August 2000Publication History 102citation888DownloadsMetricsTotal Citations102Total Downloads888Last 12 Months4Last 6 weeks0 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
Visualization and the process of modeling: a cognitive-theoretic view
Authors: Andrew W. Crapo, Laurie B. Waisel, William A. Wallace, Thomas R. Willemain
Venue: KDD
Year: 2000

Models are artifacts used to better understand our world. In the context of mining data to create knowledge, the modeler is often faced with discovering and understanding relationships in data that have no apparent analog in the laws of physical science. Much of these data are increasingly abstract; not directly related to the physical, perceivable world. The use of sketches and diagrams as aids in problem solving and as a means of communication is as old as recorded history. Can visualization help us not only to discover the patterns and relationships in these data, but also to use newly discovered knowledge to build computational models?
Data mining techniques for optimizing inventories for electronic commerce
Authors: Anjali Dhond, Amar Gupta, Sanjeev Vadhavkar
Venue: KDD
Year: 2000

As part of their strategy for incorporating electronic commerce capabilities, many organizations are involved in the development of information systems that will establish effective linkages with their suppliers, customers, and other channel partners involved in transportation, distribution, warehousing and maintenance activities.These linkages have given birth to comprehensive data warehouses that integrate operational data with supplier, customer, channel partners and market information.Data mining techniques can now provide the technological leap needed to structure and prioritize information from these data warehouses to address specific end-user problems.Emerging data mining techniques permit the semi-automatic discovery of patterns, associations, changes, anomalies, rules, and statistically significant structures and events in data.Very significant business benefits have been attained through the integration of data mining techniques with current information systems aiding electronic commerce.This paper explains key data mining principles that can play a pivotal role in an electronic commerce environment.The paper also highlights two case studies in which neural network-based data mining techniques were used for inventory optimization.The results from the data mining prototype in a large medical distribution company provided the rationale for the strategy to reduce the total level of inventory by 50% (from a billion dollars to half a billion dollars) in the particular organization, while maintaining the same level of probability that a particular customer's demand will be satisfied.The second case study highlights the use of neural network based data mining techniques for forecasting hot metal temperatures in a steel mill blast furnace.
Mining high-speed data streams
Authors: Pedro M. Domingos, Geoff Hulten
Venue: KDD
Year: 2000

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Visualization and interactive feature selection for unsupervised data
Authors: Jennifer G. Dy, Carla E. Brodley
Venue: KDD
Year: 2000

For many feature selection problems, a human de nes the features that are potentially useful, and then a subset is chosen from the original pool of features using an automated feature selection algorithm. In contrast to supervised learning, class information is not available to guide the feature search for unsupervised learning tasks. In this paper, we introduce Visual-FSSEM (Visual Feature Subset Selection using Expectation-Maximization Clustering), which incorporates visualization techniques, clustering, and user interaction to guide the feature subset search and to enable a deeper understanding of the data. Visual-FSSEM, serves both as an exploratory and multivariate-data visualization tool. We illustrate Visual-FSSEM on a high-resolution computed tomography lung image data set.
Hybrid Poisson process
Authors: Ayman Farahat
Venue: KDD
Year: 2000

Several statistical problems encountered in real world applications appear to be speci c instances of a general framework based on hybrid Poisson models. Restating these problem in this framework allows us to draw upon a large number of statistical analysis techniques and suggests a variety o f a lgorithms which p r o vide new and more robust methods for analysis. In particular, we dev elop a new method for detecting behavioral c hanges of a class of stochastic process.
A framework for specifying explicit bias for revision of approximate information extraction rules
Authors: Ronen Feldman, Yair Liberzon, Binyamin Rosenfeld, Yonatan Schler, Jonathan Stoppi
Venue: KDD
Year: 2000

Information extraction is one of the most important techniques used in Text Mining. One of the main problems in building information extraction (IE) systems is that the knowledge elicited from domain experts tends to be only approximately correct. In addition, the knowledge acquisition phase for building IE rules usually takes a tremendous amount of time on the part of the expert and of the linguist creating the rules. We therefore need an effective means of revising our IE rules whenever we discover such an inaccuracy. The IE revision problem is how best to go about revising a deficient IE rules using information contained in examples that expose inaccuracies. The revision process is very sensitive to implicit and explicit biases encoded in the specific revision algorithm employed. In a sense, each revision algorithm must provide two forms of biases: bias as to the place of the revision and bias as to the type of the revision that should be performed. In this paper we present a framework for writing approximate IE rules that are provided with explicit bias. The proposed framework can be used by many existing revision algorithms. The purpose of the revision bias framework is to allow the user to declare his own bias in a simple and structured way, i.e. to express the conditions placed on the domain knowledge for a given revision operator to be applied. This language extends and generalizes the work reported in [Feldman et. al. 1993]. It attacks the problem of writing IE rules from a novel perspective, one which enables a much faster development of IE systems.
Knowledge discovery in biological domains (tutorial AM-3)
Authors: Aris Floratos, Igor Jurisica, Isidore Rigoutsos
Venue: KDD
Year: 2000

Article Free Access Share on Knowledge discovery in biological domains (tutorial AM-3) Authors: A. Floratos IBM T.J. Watson Research IBM T.J. Watson ResearchView Profile , I. Jurisica University of Toronto University of TorontoView Profile , I. Rigoutsos IBM T.J. Watson Research IBM T.J. Watson ResearchView Profile Authors Info & Claims KDD '00: Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 95–163https://doi.org/10.1145/349093.349104Published:20 August 2000Publication History 1citation339DownloadsMetricsTotal Citations1Total Downloads339Last 12 Months8Last 6 weeks0 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 IC test data to optimize VLSI testing
Authors: Tony Fountain, Thomas G. Dietterich, Bill Sudyka
Venue: KDD
Year: 2000

We describe an application of data mining and decision analysis to the problem of die-level functional test in integrated circuit manufacturing. Integrated circuits are fabricated on large wafers that can hold hundreds of individual chips ("die"). In current practice, large and expensive machines test each of these die to check that they are functioning properly (die-level functional test; DLFT), and then the wafers are cut up, and the good die are assembled into packages and connected to the package pins. Finally, the resulting packages are tested to ensure that the final product is functioning correctly. The purpose of die-level functional test is to avoid the expense of packaging bad die and to provide rapid feedback to the fabrication process by detecting die failures. The challenge for a decision-theoretic approach is to reduce the amount of DLFT (and the associated costs) while still providing process feedback. We describe a decision-theoretic approach to DLFT in which historical test data is mined to create a probabilistic model of patterns of die failure. This model is combined with greedy value-of-information computations to decide in real time which die to test next and when to stop testing. We report the results of several experiments that demonstrate the ability of this procedure to make good testing decisions, good stopping decisions, and to detect anomalous die. Based on experiments with historical test data from Hewlett Packard Company, the resulting system has the potential to improve profits on mature IC products.
Successful customer relationship management in financial applications (tutorial PM-1)
Authors: Steve Gallant, Gregory Piatetsky-Shapiro, Dorian Pyle
Venue: KDD
Year: 2000

Article Successful customer relationship management in financial applications (tutorial PM-1) Share on Authors: Steve Gallant Knowledge Stream Partners Knowledge Stream PartnersView Profile , Gregory Piatesky-Shapiro Knowledge Stream Partners Knowledge Stream PartnersView Profile , Dorian Pyle Knowledge Stream Partners Knowledge Stream PartnersView Profile Authors Info & Claims KDD '00: Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 165–241https://doi.org/10.1145/349093.349106Online:20 August 2000Publication History 0citation2,898DownloadsMetricsTotal Citations0Total Downloads2,898Last 12 Months3Last 6 weeks0 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
Efficient algorithms for constructing decision trees with constraints
Authors: Minos N. Garofalakis, Dongjoon Hyun, Rajeev Rastogi, Kyuseok Shim
Venue: KDD
Year: 2000

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Mining the stock market (extended abstract): which measure is best?
Authors: Martin Gavrilov, Dragomir Anguelov, Piotr Indyk, Rajeev Motwani
Venue: KDD
Year: 2000

In recent years, there has been a lot of interest in the database community in mining time series data. Surprisingly, little work has been done on verifying which measures are most suitable for mining of a given class of data sets. Such work is of crucial importance, since it enables us to identify similarity measures which are useful in a given context and therefore for which efficient algorithms should be further investigated. Moreover, an accurate evaluation of the performance of even existing algorithms is not possible without a good understanding of the data sets occurring in practice.
Predictive modeling in automotive direct marketing: tools, experiences and open issues
Authors: Wendy Gersten, Rüdiger Wirth, Dirk Arndt
Venue: KDD
Year: 2000

Direct marketing is an increasingly popular application of data mining. In this paper we summarize some of our own experiences from various data mining application projects for direct marketing. We focus on a particular project environment and describe tools which address issues across the whole data mining process. These tools include a Quick Reference Guide for the standardization of the process and for user guidance and a library of re-usable procedures in the commercial data mining tool Clementine. We report experiences with these tools and identify open issues requiring further research. In particular, we focus on evaluation measures for predictive models.
Time series similarity measures (tutorial PM-2)
Authors: Dimitrios Gunopulos, Gautam Das
Venue: KDD
Year: 2000

A time series is a sequence of real numbers, representing the measurements of a real variable at equal time intervals -Stock price movements -V o l u m e o f sales over time -Daily temperature readings
RuleViz: a model for visualizing knowledge discovery process
Authors: Jianchao Han, Nick Cercone
Venue: KDD
Year: 2000

Article Free Access Share on RuleViz: a model for visualizing knowledge discovery process Authors: Jianchao Han Department of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1 Canada Department of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1 CanadaView Profile , Nick Cercone Department of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1 Canada Department of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1 CanadaView Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000Pages 244–253https://doi.org/10.1145/347090.347139Published:01 August 2000Publication History 38citation1,220DownloadsMetricsTotal Citations38Total Downloads1,220Last 12 Months84Last 6 weeks8 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
Visualizing association rules with interactive mosaic plots
Authors: Heike Hofmann, Arno Siebes, Adalbert F. X. Wilhelm
Venue: KDD
Year: 2000

Association rules are amongst the most important patterns that can be discovered using data mining. Their automatic discovery is supported by most, if not all, data mining software tools. Moreover, many techniques have been devised to lter the most interesting (in many senses) rules from the complete set of discovered rules, such that the users are not swamped by results. However, association rules are actually hard to understand; even more so if one only looks at the most interesting rules. For example, rather strong correlations between attributes are not always obvious from the discovered rules. Similarly, a deeper explanation of related association rules may be missing from the rule set. In this paper we show how Mosaic plots and, especially, their variant called Double Decker plots, can be used to visualize association rules. These plots visualize the contingency table that yields the association rule as well as the other potential rules in that table, whether they meet the thresholds or not. This gives a deeper understanding on the nature of the correlation between the left-hand side of the rule and the right-hand side. Moreover, we show how an interactive use of these plots helps the user to understand the relationship between related association rules.
Exploration mining in diabetic patients databases: findings and conclusions
Authors: Wynne Hsu, Mong-Li Lee, Bing Liu, Tok Wang Ling
Venue: KDD
Year: 2000

Real-life data mining applications are interesting because they often present a different set of problems for data miners. One such real-life application that we have done is on the diabetic patients databases. Valuable lessons are learnt from this application. In particular, we discover that the often neglected pre-processing and post-processing steps in knowledge discovery are the most critical elements in determining the success of a real-life data mining application. In this paper, we shall discuss how we carry out knowledge discovery on this diabetic patient database, the interesting issues that have surfaced, as well as the lessons we have learnt from this application. We will describe a semi-automatic means for cleaning the diabetic patient database, and present a step-by-step approach to help the health doctors explore their data and to understand the discovered rules better.
Visualizing high dimensional datasets and multivariate relations (tutorial AM-2)
Authors: Alfred Inselberg
Venue: KDD
Year: 2000

Article Free Access Share on Visualizing high dimensional datasets and multivariate relations (tutorial AM-2) Author: Alfred Inselberg Tel Aviv University Tel Aviv UniversityView Profile Authors Info & Claims KDD '00: Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 33–94https://doi.org/10.1145/349093.349102Published:20 August 2000Publication History 3citation955DownloadsMetricsTotal Citations3Total Downloads955Last 12 Months7Last 6 weeks0 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