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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 100 Knowledge discovery — A control theory perspective
  3. Altmetric Badge
    Chapter 101 Modelling customer retention with Rough Data Models
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    Chapter 102 Share based measures for itemsets
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    Chapter 103 Parallel knowledge discovery using domain generalization graphs
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    Chapter 104 Rough set theory and rule induction techniques for discovery of attribute dependencies in medical information systems
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    Chapter 105 Logical calculi for knowledge discovery in databases
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    Chapter 106 Extraction of experts' decision process from clinical databases using rough set model
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    Chapter 107 Discovering of health risks and case-based forecasting of epidemics in a health surveillance system
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    Chapter 108 An algorithm for multi-relational discovery of subgroups
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    Chapter 109 Finding similar time series
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    Chapter 110 Exploration of document collections with self-organizing maps: A novel approach to similarity representation
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    Chapter 111 Pattern based browsing in document collections
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    Chapter 112 Induction of fuzzy characteristic rules
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    Chapter 113 Regression-based classification methods and their comparison with decision tree algorithms
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    Chapter 114 Attribute discovery and rough sets
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    Chapter 115 Generation of rules from incomplete information systems
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    Chapter 116 Knowledge discovery from software engineering data: Rough set analysis and its interaction with goal-oriented measurement
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    Chapter 117 Efficient multisplitting on numerical data
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    Chapter 118 SNOUT: An intelligent assistant for exploratory data analysis
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    Chapter 119 Exploratory analysis of biochemical processes using hybrid modeling methods
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    Chapter 120 Using signature files for querying time-series data
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    Chapter 121 A new and versatile method for association generation
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    Chapter 122 Bivariate decision trees
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    Chapter 123 Towards process-oriented tool support for knowledge discovery in databases
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    Chapter 124 A connectionist approach to structural similarity determination as a basis of clustering, classification and feature detection
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    Chapter 125 Searching for relational patterns in data
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    Chapter 126 Finding spatial clusters
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    Chapter 127 Interactive interpretation of hierarchical clustering
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    Chapter 128 The principle of transformation between efficiency and effectiveness: Towards a fair evaluation of the cost-effectiveness of KDD techniques
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    Chapter 129 Recognizing reliability of discovered knowledge
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    Chapter 130 Clustering techniques in biological sequence analysis
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    Chapter 131 TOAS intelligence mining; analysis of natural language processing and computational linguistics
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    Chapter 132 Algorithms for constructing of decision trees
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    Chapter 133 Mining in the phrasal frontier
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    Chapter 134 Mining time series using rough sets — A case study
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    Chapter 135 Neural networks design: Rough set approach to continuous data
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    Chapter 136 On meta levels of an organized society of KDD agents
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    Chapter 137 Using neural network to extract knowledge from database
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    Chapter 138 Induction of strong feature subsets
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    Chapter 139 Rough sets for data mining and knowledge discovery
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    Chapter 140 Techniques and applications of KDD
  43. Altmetric Badge
    Chapter 141 A tutorial introduction to high performance data mining
  44. Altmetric Badge
    Chapter 142 Data mining in the telecommunications industry
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

facebook
1 Facebook page
wikipedia
2 Wikipedia pages

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
110 Mendeley
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Title
Principles of Data Mining and Knowledge Discovery
Published by
Lecture notes in computer science, January 1997
DOI 10.1007/3-540-63223-9
ISBNs
978-3-54-063223-8, 978-3-54-069236-2
Authors

Jan Komorowski, Jan Zytkow

Editors

Jan Komorowski, Jan Zytkow

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 4 4%
Belgium 2 2%
United Kingdom 1 <1%
Italy 1 <1%
Brazil 1 <1%
Slovenia 1 <1%
United States 1 <1%
Japan 1 <1%
Argentina 1 <1%
Other 1 <1%
Unknown 96 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 29%
Student > Master 30 27%
Researcher 14 13%
Student > Bachelor 11 10%
Unspecified 5 5%
Other 18 16%
Readers by discipline Count As %
Computer Science 60 55%
Engineering 12 11%
Unspecified 8 7%
Mathematics 8 7%
Business, Management and Accounting 5 5%
Other 17 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 20 May 2014.
All research outputs
#7,215,016
of 22,805,349 outputs
Outputs from Lecture notes in computer science
#2,401
of 8,126 outputs
Outputs of similar age
#19,426
of 91,561 outputs
Outputs of similar age from Lecture notes in computer science
#8
of 20 outputs
Altmetric has tracked 22,805,349 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 8,126 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 69% 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 91,561 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.