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Principles of Data Mining and Knowledge Discovery

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Cover of 'Principles of Data Mining and Knowledge Discovery'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Scaling up Dynamic Time Warping to Massive Datasets
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    Chapter 2 The Haar Wavelet Transform in the Time Series Similarity Paradigm
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    Chapter 3 Rule Discovery in Large Time-Series Medical Databases
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    Chapter 4 Simultaneous Prediction of Multiple Chemical Parameters of River Water Quality with TILDE
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    Chapter 5 Applying Data Mining Techniques to Wafer Manufacturing
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    Chapter 6 An Application of Data Mining to the Problem of the University Students’ Dropout Using Markov Chains
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    Chapter 7 Discovering and Visualizing Attribute Associations Using Bayesian Networks and Their Use in KDD
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    Chapter 8 Taxonomy Formation by Approximate Equivalence Relations, Revisited
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    Chapter 9 On the Use of Self-Organizing Maps for Clustering and Visualization
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    Chapter 10 Speeding Up the Search for Optimal Partitions
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    Chapter 11 Experiments in Meta-level Learning with ILP
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    Chapter 12 Boolean Reasoning Scheme with Some Applications in Data Mining
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    Chapter 13 On the Correspondence between Classes of Implicational and Equivalence Quantifiers
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    Chapter 14 Querying Inductive Databases via Logic-Based User-Defined Aggregates
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    Chapter 15 Peculiarity Oriented Multi-database Mining
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    Chapter 16 Knowledge Discovery in Medical Multi-databases: A Rough Set Approach
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    Chapter 17 Automated Discovery of Rules and Exceptions from Distributed Databases Using Aggregates
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    Chapter 18 Text Mining via Information Extraction
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    Chapter 19 TopCat: Data Mining for Topic Identification in a Text Corpus
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    Chapter 20 Selection and Statistical Validation of Features and Prototypes
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    Chapter 21 Taming Large Rule Models in Rough Set Approaches
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    Chapter 22 Optimizing Disjunctive Association Rules
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    Chapter 23 Contribution of Boosting in Wrapper Models
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    Chapter 24 Experiments on a Representation-Independent “Top-Down and Prune” Induction Scheme
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    Chapter 25 Heuristic Measures of Interestingness
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    Chapter 26 Enhancing Rule Interestingness for Neuro-fuzzy Systems
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    Chapter 27 Unsupervised Profiling for Identifying Superimposed Fraud
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    Chapter 28 OPTICS-OF: Identifying Local Outliers
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    Chapter 29 Selective Propositionalization for Relational Learning
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    Chapter 30 Circle Graphs: New Visualization Tools for Text-Mining
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    Chapter 31 On the Consistency of Information Filters for Lazy Learning Algorithms
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    Chapter 32 Using Genetic Algorithms to Evolve a Rule Hierarchy
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    Chapter 33 Mining Temporal Features in Association Rules
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    Chapter 34 The Improvement of Response Modeling: Combining Rule-Induction and Case-Based Reasoning
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    Chapter 35 Analyzing an Email Collection Using Formal Concept Analysis
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    Chapter 36 Business Focused Evaluation Methods: A Case Study
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    Chapter 37 Combining Data and Knowledge by MaxEnt-Optimization of Probability Distributions
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    Chapter 38 Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation?
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    Chapter 39 Rough Dependencies as a Particular Case of Correlation: Application to the Calculation of Approximative Reducts
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    Chapter 40 A Fuzzy Beam-Search Rule Induction Algorithm
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    Chapter 41 An Innovative GA-Based Decision Tree Classifier in Large Scale Data Mining
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    Chapter 42 Extension to C-means Algorithm for the Use of Similarity Functions
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    Chapter 43 Predicting Chemical Carcinogenesis Using Structural Information Only
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    Chapter 44 LA – A Clustering Algorithm with an Automated Selection of Attributes, Which is Invariant to Functional Transformations of Coordinates
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    Chapter 45 Association Rule Selection in a Data Mining Environment
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    Chapter 46 Multi-relational Decision Tree Induction
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    Chapter 47 Learning of Simple Conceptual Graphs from Positive and Negative Examples
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    Chapter 48 An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction
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    Chapter 49 ZigZag, a New Clustering Algorithm to Analyze Categorical Variable Cross-Classification Tables
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    Chapter 50 Efficient Mining of High Confidence Association Rules without Support Thresholds
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    Chapter 51 A Logical Approach to Fuzzy Data Analysis
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    Chapter 52 AST: Support for Algorithm Selection with a CBR Approach
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    Chapter 53 Efficient Shared Near Neighbours Clustering of Large Metric Data Sets
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    Chapter 54 Discovery of “Interesting” Data Dependencies from a Workload of SQL Statements
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    Chapter 55 Learning from Highly Structured Data by Decomposition
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    Chapter 56 Combinatorial Approach for Data Binarization
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    Chapter 57 Extending Attribute-Oriented Induction as a Key-Preserving Data Mining Method
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    Chapter 58 Automated Discovery of Polynomials by Inductive Genetic Programming
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    Chapter 59 Diagnosing Acute Appendicitis with Very Simple Classification Rules
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    Chapter 60 Rule Induction in Cascade Model Based on Sum of Squares Decomposition
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    Chapter 61 Maintenance of Discovered Knowledge
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    Chapter 62 A Divisive Initialisation Method for Clustering Algorithms
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    Chapter 63 A Comparison of Model Selection Procedures for Predicting Turning Points in Financial Time Series
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    Chapter 64 Mining Lemma Disambiguation Rules from Czech Corpora
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    Chapter 65 Adding Temporal Semantics to Association Rules
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    Chapter 66 Studying the Behavior of Generalized Entropy in Induction Trees Using a M-of-N Concept
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    Chapter 67 Discovering Rules in Information Trees
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    Chapter 68 Mining Text Archives: Creating Readable Maps to Structure and Describe Document Collections
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    Chapter 69 Neuro-fuzzy Data Mining for Target Group Selection in Retail Banking
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    Chapter 70 Mining Possibilistic Set-Valued Rules by Generating Prime Disjunctions
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    Chapter 71 Towards Discovery of Information Granules
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    Chapter 72 Classification Algorithms Based on Linear Combinations of Features
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    Chapter 73 Managing Interesting Rules in Sequence Mining
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    Chapter 74 Support Vector Machines for Knowledge Discovery
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    Chapter 75 Regression by Feature Projections
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    Chapter 76 Generating Linguistic Fuzzy Rules for Pattern Classification with Genetic Algorithms
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    Chapter 77 Data Mining for Robust Business Intelligence Solutions
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    Chapter 78 Query Languages for Knowledge Discovery in Databases
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    Chapter 79 The ESPRIT Project CreditMine and its Relevance for the Internet Market
  81. Altmetric Badge
    Chapter 80 Logics and Statistics for Association Rules and Beyond
  82. Altmetric Badge
    Chapter 81 Data Mining for the Web
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    Chapter 82 Relational Learning and Inductive Logic Programming Made Easy
Attention for Chapter 28: OPTICS-OF: Identifying Local Outliers
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Chapter title
OPTICS-OF: Identifying Local Outliers
Chapter number 28
Book title
Principles of Data Mining and Knowledge Discovery
Published in
Lecture notes in computer science, September 1999
DOI 10.1007/978-3-540-48247-5_28
Book ISBNs
978-3-54-066490-1, 978-3-54-048247-5
Authors

Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jörg Sander

Mendeley readers

The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
United Kingdom 1 <1%
Brazil 1 <1%
India 1 <1%
Ireland 1 <1%
Canada 1 <1%
Mexico 1 <1%
Korea, Republic of 1 <1%
Japan 1 <1%
Other 1 <1%
Unknown 116 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 40 31%
Student > Master 32 25%
Researcher 17 13%
Student > Bachelor 11 9%
Other 6 5%
Other 18 14%
Unknown 5 4%
Readers by discipline Count As %
Computer Science 77 60%
Engineering 20 16%
Physics and Astronomy 4 3%
Mathematics 4 3%
Biochemistry, Genetics and Molecular Biology 3 2%
Other 12 9%
Unknown 9 7%