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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

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    Book Overview
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    Chapter 1 On objective measures of rule surprisingness
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    Chapter 2 Discovery of surprising exception rules based on intensity of implication
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    Chapter 3 A metric for selection of the most promising rules
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    Chapter 4 For visualization-based analysis tools in knowledge discovery process: A multilayer perceptron versus principal components analysis: A comparative study
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    Chapter 5 Trend graphs: Visualizing the evolution of concept relationships in large document collections
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    Chapter 6 Ranked rules and data visualization
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    Chapter 7 TextVis: An integrated visual environment for text mining
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    Chapter 8 Text mining at the term level
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    Chapter 9 A new algorithm for faster mining of generalized association rules
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    Chapter 10 Knowledge discovery with clustering based on rules. Interpreting results
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    Chapter 11 Efficient construction of comprehensible hierarchical clusterings
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    Chapter 12 Cost sensitive discretization of numeric attributes
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    Chapter 13 Handling KDD process changes by incremental replanning
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    Chapter 14 Object mining: A practical application of data mining for the construction and maintenance of software components
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    Chapter 15 A relational data mining tool based on genetic programming
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    Chapter 16 Inducing cost-sensitive trees via instance weighting
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    Chapter 17 Model switching for bayesian classification trees with soft splits
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    Chapter 18 Interactive visualisation for predictive modelling with decision tree induction
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    Chapter 19 Discovery of diagnostic patterns from protein sequence databases
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    Chapter 20 The PSP approach for mining sequential patterns
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    Chapter 21 Knowledge discovery in spatial data by means of ILP
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    Chapter 22 Querying inductive databases: A case study on the MINE RULE operator
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    Chapter 23 Classes of four-fold table quantifiers
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    Chapter 24 Detection of interdependences in attribute selection
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    Chapter 25 Postponing the evaluation of attributes with a high number of boundary points
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    Chapter 26 A hybrid approach to feature selection
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    Chapter 27 Discretization and grouping: Preprocessing steps for data mining
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    Chapter 28 Fuzzy spatial OQL for fuzzy knowledge discovery in databases
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    Chapter 29 Extended functional dependencies as a basis for linguistic summaries
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    Chapter 30 A comparison of batch and incremental supervised learning algorithms
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    Chapter 31 Knowledge Discovery with qualitative influences and synergies
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    Chapter 32 Language support for temporal data mining
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    Chapter 33 Resampling in an indefinite database to approximate functional dependencies
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    Chapter 34 Knowledge discovery from client-server databases
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    Chapter 35 Discovery of common subsequences in cognitive evoked potentials
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    Chapter 36 Improving the discovery of association rules with intensity of implication
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    Chapter 37 Generalization lattices
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    Chapter 38 Overcoming fragmentation in decision trees through attribute value grouping
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    Chapter 39 Data mining at a major bank: Lessons from a large marketing application
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    Chapter 40 PolyAnalyst data analysis technique and its specialization for processing data organized as a set of attribute values
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    Chapter 41 Representative association rules and minimum condition maximum consequence association rules
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    Chapter 42 Discovery of decision rules from databases: An evolutionary approach
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    Chapter 43 Using loglinear clustering for subcategorization identification
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    Chapter 44 Exploratory attributes search for time-series data: An experimental system for agricultural application
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    Chapter 45 A procedure to compute prototypes for data mining in non-structured domains
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    Chapter 46 From the data mine to the knowledge mill: Applying the principles of lexical analysis to the data mining and knowledge discovery process
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    Chapter 47 Preprocessing of missing values using robust association rules
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    Chapter 48 Similarity-driven sampling for data mining
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    Chapter 49 Modeling the business process by mining multiple databases
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    Chapter 50 Data transformation and rough sets
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    Chapter 51 Conceptual Knowledge Discovery in Databases using formal concept analysis methods
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    Chapter 52 Clasitex+: A tool for knowledge discovery from texts
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    Chapter 53 Discovery of approximate medical knowledge based on rough set model
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    Chapter 54 Scalable, high-performance data mining with parallel processing
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    Chapter 55 Practical text mining
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    Chapter 56 Industrial applications of data mining
Overall attention for this book and its chapters
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Citations

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Readers on

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19 Mendeley
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Title
Principles of Data Mining and Knowledge Discovery
Published by
Springer, Berlin, Heidelberg , January 1998
DOI 10.1007/bfb0094798
ISBNs
978-3-54-065068-3, 978-3-54-049687-8
Editors

Jan M. Żytkow, Mohamed Quafafou

View on publisher site
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 11%
India 1 5%
Brazil 1 5%
United Kingdom 1 5%
Unknown 14 74%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 26%
Student > Ph. D. Student 4 21%
Student > Master 3 16%
Professor > Associate Professor 3 16%
Professor 2 11%
Other 2 11%
Readers by discipline Count As %
Computer Science 14 74%
Unspecified 2 11%
Engineering 2 11%
Economics, Econometrics and Finance 1 5%

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