Principles of Data Mining and Knowledge Discovery : Third European Conference, PKDD'99, Prague, Czech Republic, September 15-18, 1999, Proceedings
by Pkdd 9 (1999 Prague, Czech Republic); Rauch, Jan; Carbonell, J. G.; Siekmann, J.; Zytkow, Jan M.Format: Paperback
Pub. Date: 1999-10-01
Publisher(s): Springer-Verlag New York Inc
Other versions by this Author
-
Receive Free Shipping To The More Store!*
*Marketplace items do not qualify for the free shipping promotion.
-
eCampus.com Device Compatibility Matrix
Click the device icon to install or view instructions
Apple iOS | iPad, iPhone, iPod
Android Devices | Android Tables & Phones OS 2.2 or higher | *Kindle Fire
Windows 10 / 8 / 7 / Vista / XP
Mac OS X | **iMac / MacbookEnjoy offline reading with these devicesiPad, iPhone, iPod
Android 2.2 +Kindle FireWindows
10 / 8 / 7 / Vista / XPMac
List Price: $149.00
Rent Textbook
Select for Price
Rent Digital
$77.22
New Textbook
We're Sorry
Sold Out
Used Textbook
We're Sorry
Sold Out
How Marketplace Works:
- This item is offered by an independent seller and not shipped from our warehouse
- Item details like edition and cover design may differ from our description; see seller's comments before ordering.
- Sellers much confirm and ship within two business days; otherwise, the order will be cancelled and refunded.
- Marketplace purchases cannot be returned to eCampus.com. Contact the seller directly for inquiries; if no response within two days, contact customer service.
- Additional shipping costs apply to Marketplace purchases. Review shipping costs at checkout.
Summary
This book constitutes the refereed proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999.The 28 revised full papers and 48 poster presentations were carefully reviewed and selected from 106 full papers submitted. The papers are organized in topical sections on time series, applications, taxonomies and partitions, logic methods, distributed and multirelational databases, text mining and feature selection, rules and induction, and interesting and unusual issues.
Table of Contents
| - Time Series | |
| Scaling up Dynamic Time Warping to Massive Dataset | p. 1 |
| The Haar Wavelet Transform in the Time Series Similarity Paradigm | p. 12 |
| Rule Discovery in Large Time-Series Medical Databases | p. 23 |
| - Applications | |
| Simultaneous Prediction of Multiple Chemical Parameters of River Water Quality with TILDE | p. 32 |
| Applying Data Mining Techniques to Wafer Manufacturing | p. 41 |
| An Application of Data Mining to the Problem of the University Students' Dropout Using Markov Chains | p. 51 |
| - Taxonomies and Partitions | |
| Discovering and Visualizing Attribute Associations Using Bayesian Networks and Their Use in KDD | p. 61 |
| Taxonomy Formation by Approximate Equivalence Relations, Revisited | p. 71 |
| On the Use of Self-Organizing Maps for Clustering and Visualization | p. 80 |
| Speeding Up the Search for Optimal Partitions | p. 89 |
| - Logic Methods | |
| Experiments in Meta-level Learning with ILP | p. 98 |
| Boolean Reasoning Scheme with Some Applications in Data Mining | p. 107 |
| On the Correspondence between Classes of Implicational and Equivalence Quantifiers | p. 116 |
| Querying Inductive Databases via Logic-Based User-Defined Aggregates | p. 125 |
| - Distributed and Multirelational Databases | |
| Peculiarity Oriented Multi-database Mining | p. 136 |
| Knowledge Discovery in Medical Multi-databases: A Rough Set Approach | p. 147 |
| Automated Discovery of Rules and Exceptions from Distributed Databases Using Aggregates | p. 156 |
| - Text Mining and Feature Selection | |
| Text Mining via Information Extraction | p. 165 |
| TopCat: Data Mining for Topic Identification in a Text Corpus | p. 174 |
| Selection and Statistical Validation of Features and Prototypes | p. 184 |
| - Rules and Induction | |
| Taming Large Rule Models in Rough Set Approaches | p. 193 |
| Optimizing Disjunctive Association Rules | p. 204 |
| Contribution of Boosting in Wrapper Models | p. 214 |
| Experiments on a Representation-Independent "Top-Down and Prune" Induction Scheme | p. 223 |
| - Interesting and Unusual | |
| Heuristic Measures of Interestingness | p. 232 |
| Enhancing Rule Interestingness for Neuro-fuzzy Systems | p. 242 |
| Unsupervised Profiling for Identifying Superimposed Fraud | p. 251 |
| OPTICS-OF: Identifying Local Outliers | p. 262 |
| Posters | |
| Selective Propositionalization for Relational Learning | p. 271 |
| Circle Graphs: New Visualization Tools for Text-Mining | p. 277 |
| On the Consistency of Information Filters for Lazy Learning Algorithms | p. 283 |
| Using Genetic Algorithms to Evolve a Rule Hierarchy | p. 289 |
| Mining Temporal Features in Association Rules | p. 295 |
| The Improvement of Response Modeling: Combining Rule-Induction and Case-Based Reasoning | p. 301 |
| Analyzing an Email Collection Using Formal Concept Analysis | p. 309 |
| Business Focused Evaluation Methods: A Case Study | p. 316 |
| Combining Data and Knowledge by MaxEnt-Optimization of Probability Distributions | p. 323 |
| Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation? | p. 329 |
| Rough Dependencies as a Particular Case of Correlation: Application to the Calculation of Approximative Reducts | p. 335 |
| A Fuzzy Beam-Search Rule Induction Algorithm | p. 341 |
| An Innovative GA-Based Decision Tree Classifier in Large Scale Data Mining | p. 348 |
| Extension to C-means Algorithm for the Use of Similarity Functions | p. 354 |
| Predicting Chemical Carcinogenesis Using Structural Information Only | p. 360 |
| LA - A Clustering Algorithm with an Automated Selection of Attributes, which Is Invariant to Functional Transformations of Coordinates | p. 366 |
| Association Rule Selection in a Data Mining Environment | p. 372 |
| Multi-relational Decision Tree Induction | p. 378 |
| Learning of Simple Conceptual Graphs from Positive and Negative Examples | p. 384 |
| An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction | p. 392 |
| ZigZag, a New Clustering Algorithm to Analyze Categorical Variable Cross-Classification Tables | p. 398 |
| Efficient Mining of High Confidence Association Rules without Support Thresholds | p. 406 |
| A Logical Approach to Fuzzy Data Analysis | p. 412 |
| AST: Support for Algorithm Selection with a CBR Approach | p. 418 |
| Efficient Shared Near Neighbours Clustering of Large Metric Data Sets | p. 424 |
| Discovery of "Interesting" Data Dependencies from a Workload of SQL Statements | p. 430 |
| Learning from Highly Structured Data by Decomposition | p. 436 |
| Combinatorial Approach for Data Binarization | p. 442 |
| Extending Attribute-Oriented Induction as a Key-Preserving Data Mining Method | p. 448 |
| Automated Discovery of Polynomials by Inductive Genetic Programming | p. 456 |
| Diagnosing Acute Appendicitis with Very Simple Classification Rules | p. 462 |
| Rule Induction in Cascade Model Based on Sum of Squares Decomposition | p. 468 |
| Maintenance of Discovered Knowledge | p. 476 |
| A Divisive Initialization Method for Clustering Algorithms | p. 484 |
| A Comparison of Model Selection Procedures for Predicting Turning Points in Financial Time Series | p. 492 |
| Mining Lemma Disambiguation Rules from Czech Corpora | p. 498 |
| Adding Temporal Semantics to Association Rules | p. 504 |
| Studying the Behavior of Generalized Entropy in Induction Trees Using a M-of-N Concept | p. 510 |
| Discovering Rules in Information Trees | p. 518 |
| Mining Text Archives: Creating Readable Maps to Structure and Describe Document Collections | p. 524 |
| Neuro-fuzzy Data Mining for Target Group Selection in Retail Banking | p. 530 |
| Mining Possibilistic Set-Valued Rules by Generating Prime Disjunctions | p. 536 |
| Towards Discovery of Information Granules | p. 542 |
| Classification Algorithms Based on Linear Combinations of Features | p. 548 |
| Managing Interesting Rules in Sequence Mining | p. 554 |
| Support Vector Machines for Knowledge Discovery | p. 561 |
| Regression by Feature Projections | p. 568 |
| Generating Linguistic Fuzzy Rules for Pattern Classification with Genetic Algorithms | p. 574 |
| Tutorials | |
| Data Mining for Robust Business Intelligence Solutions | p. 580 |
| Query Languages for Knowledge Discovery in Databases | p. 582 |
| The ESPRIT Project CreditMine and Its Relevance for the Internet Market | p. 584 |
| Logics and Statistics for Association Rules and Beyond | p. 586 |
| Data Mining for the Web | p. 588 |
| Relational Learning and Inductive Logic Programming Made Easy | p. 590 |
| Author Index | p. 591 |
| Table of Contents provided by Publisher. All Rights Reserved. |
An electronic version of this book is available through VitalSource.
This book is viewable on PC, Mac, iPhone, iPad, iPod Touch, and most smartphones.
By purchasing, you will be able to view this book online, as well as download it, for the chosen number of days.
Digital License
You are licensing a digital product for a set duration. Durations are set forth in the product description, with "Lifetime" typically meaning five (5) years of online access and permanent download to a supported device. All licenses are non-transferable.
More details can be found here.
A downloadable version of this book is available through the eCampus Reader or compatible Adobe readers.
Applications are available on iOS, Android, PC, Mac, and Windows Mobile platforms.
Please view the compatibility matrix prior to purchase.