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Principles of Data Mining and Knowledge Discovery

Overview of attention for book
Cover of 'Principles of Data Mining and Knowledge Discovery'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Optimized Substructure Discovery for Semi-structured Data
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    Chapter 2 Fast Outlier Detection in High Dimensional Spaces
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    Chapter 3 Data Mining in Schizophrenia Research — Preliminary Analysis
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    Chapter 4 Fast Algorithms for Mining Emerging Patterns
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    Chapter 5 On the Discovery of Weak Periodicities in Large Time Series
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    Chapter 6 The Need for Low Bias Algorithms in Classification Learning from Large Data Sets
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    Chapter 7 Mining All Non-derivable Frequent Itemsets
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    Chapter 8 Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance
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    Chapter 9 Finding Association Rules with Some Very Frequent Attributes
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    Chapter 10 Unsupervised Learning: Self-aggregation in Scaled Principal Component Space*
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    Chapter 11 A Classification Approach for Prediction of Target Events in Temporal Sequences
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    Chapter 12 Privacy-Oriented Data Mining by Proof Checking
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    Chapter 13 Choose Your Words Carefully: An Empirical Study of Feature Selection Metrics for Text Classification
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    Chapter 14 Generating Actionable Knowledge by Expert-Guided Subgroup Discovery
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    Chapter 15 Clustering Transactional Data
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    Chapter 16 Multiscale Comparison of Temporal Patterns in Time-Series Medical Databases
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    Chapter 17 Association Rules for Expressing Gradual Dependencies
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    Chapter 18 Support Approximations Using Bonferroni-Type Inequalities
  20. Altmetric Badge
    Chapter 19 Using Condensed Representations for Interactive Association Rule Mining
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    Chapter 20 Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting
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    Chapter 21 Dependency Detection in MobiMine and Random Matrices
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    Chapter 22 Long-Term Learning for Web Search Engines
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    Chapter 23 Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database
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    Chapter 24 Involving Aggregate Functions in Multi-relational Search
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    Chapter 25 Information Extraction in Structured Documents Using Tree Automata Induction
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    Chapter 26 Algebraic Techniques for Analysis of Large Discrete-Valued Datasets
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    Chapter 27 Geography of Di.erences between Two Classes of Data
  29. Altmetric Badge
    Chapter 28 Rule Induction for Classification of Gene Expression Array Data
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    Chapter 29 Clustering Ontology-Based Metadata in the Semantic Web
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    Chapter 30 Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases
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    Chapter 31 SVM Classification Using Sequences of Phonemes and Syllables
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    Chapter 32 A Novel Web Text Mining Method Using the Discrete Cosine Transform
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    Chapter 33 A Scalable Constant-Memory Sampling Algorithm for Pattern Discovery in Large Databases
  35. Altmetric Badge
    Chapter 34 Answering the Most Correlated N Association Rules Efficiently
  36. Altmetric Badge
    Chapter 35 Mining Hierarchical Decision Rules from Clinical Databases Using Rough Sets and Medical Diagnostic Model
  37. Altmetric Badge
    Chapter 36 Efficiently Mining Approximate Models of Associations in Evolving Databases
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    Chapter 37 Explaining Predictions from a Neural Network Ensemble One at a Time
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    Chapter 38 Structuring Domain-Specific Text Archives by Deriving a Probabilistic XML DTD
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    Chapter 39 Separability Index in Supervised Learning
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    Chapter 40 Finding Hidden Factors Using Independent Component Analysis
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    Chapter 41 Reasoning with Classifiers*
  43. Altmetric Badge
    Chapter 42 A Kernel Approach for Learning from Almost Orthogonal Patterns
  44. Altmetric Badge
    Chapter 43 Learning with Mixture Models: Concepts and Applications
Attention for Chapter 2: Fast Outlier Detection in High Dimensional Spaces
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

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5 Dimensions

Readers on

mendeley
161 Mendeley
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Chapter title
Fast Outlier Detection in High Dimensional Spaces
Chapter number 2
Book title
Principles of Data Mining and Knowledge Discovery
Published in
Lecture notes in computer science, September 2002
DOI 10.1007/3-540-45681-3_2
Book ISBNs
978-3-54-044037-6, 978-3-54-045681-0
Authors

Fabrizio Angiulli, Clara Pizzuti

Editors

Tapio Elomaa, Heikki Mannila, Hannu Toivonen

Mendeley readers

The data shown below were compiled from readership statistics for 161 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Ireland 1 <1%
Norway 1 <1%
France 1 <1%
Brazil 1 <1%
United Kingdom 1 <1%
Canada 1 <1%
Germany 1 <1%
Unknown 152 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 46 29%
Student > Master 36 22%
Researcher 17 11%
Student > Bachelor 11 7%
Other 9 6%
Other 19 12%
Unknown 23 14%
Readers by discipline Count As %
Computer Science 89 55%
Engineering 23 14%
Mathematics 4 2%
Agricultural and Biological Sciences 3 2%
Business, Management and Accounting 3 2%
Other 8 5%
Unknown 31 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 January 2015.
All research outputs
#3,583,910
of 12,390,159 outputs
Outputs from Lecture notes in computer science
#2,048
of 7,264 outputs
Outputs of similar age
#71,777
of 233,717 outputs
Outputs of similar age from Lecture notes in computer science
#25
of 152 outputs
Altmetric has tracked 12,390,159 research outputs across all sources so far. This one is in the 49th percentile – i.e., 49% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,264 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 233,717 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 152 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.