Knowledge discovery from data streams:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
London
Chapman & Hall
2010
|
| Schriftenreihe: | Chapman & Hall/CRC data mining and knowledge discovery series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XIX, 237 S. graph. Darst. |
| ISBN: | 9781439826119 1439826110 |
Internformat
MARC
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Datensatz im Suchindex
| _version_ | 1804141152673202176 |
|---|---|
| adam_text | Contents
List of Tables
xi
List of Figures
xiii
List of Algorithms
xv
Foreword
xvii
Acknowledgments
xix
1
Knowledge Discovery from Data Streams
1
1.1
Introduction
........................... 1
1.2
An Illustrative Example
..................... 2
1.3
A World in Movement
...................... 4
1.4
Data Mining and Data Streams
................ 5
2
Introduction to Data Streams
7
2.1
Data Stream Models
....................... 7
2.1.1
Research Issues in Data Stream Management Systems
8
2.1.2
An Illustrative Problem
................. 8
2.2
Basic Streaming Methods
.................... 9
2.2.1
Illustrative Examples
................... 10
2.2.1.1
Counting the Number of Occurrences of the
Elements in a Stream
............. 10
2.2.1.2
Counting the Number of Distinct Values in a
Stream
..................... 11
2.2.2
Bounds of Random Variables
.............. 11
2.2.3
Poisson
Processes
..................... 13
2.2.4
Maintaining Simple Statistics from Data Streams
... 14
2.2.5
Sliding Windows
..................... 14
2.2.5.1
Computing Statistics over Sliding Windows:
TheADWIN Algorithm
............. 16
2.2.6
Data Synopsis
...................... 19
2.2.6.1
Sampling
.................... 19
2.2.6.2
Synopsis and Histograms
........... 20
2.2.6.3
Wavelets
.................... 21
2.2.6.4
Discrete Fourier Transform
.......... 22
vi
Knowledge Discovery
from
Data
Streams
2.3 Illustrative Applications..................... 23
2.3.1
A Data Warehouse
Problem:
Hot-Lists.........
23
2.3.2 Computing
the Entropy in a Stream
.......... 24
2.3.3
Monitoring Correlations Between Data Streams
.... 27
2.3.4
Monitoring Threshold Functions over Distributed Data
Streams
.......................... 29
2.4
Notes
............................... 30
3
Change Detection
33
3.1
Introduction
........................... 33
3.2
Tracking Drifting Concepts
................... 34
3.2.1
The Nature of Change
.................. 35
3.2.2
Characterization of Drift Detection Methods
..... 36
3.2.2.1
Data Management
............... 37
3.2.2.2
Detection Methods
............... 38
3.2.2.3
Adaptation Methods
.............. 40
3.2.2.4
Decision Model Management
......... 41
3.2.3
A Note on Evaluating Change Detection Methods
. . 41
3.3
Monitoring the Learning Process
................ 42
3.3.1
Drift Detection Using Statistical Process Control
... 42
3.3.2
An Illustrative Example
................. 45
3.4
Final Remarks
.......................... 46
3.5
Notes
............................... 47
4
Maintaining Histograms from Data Streams
49
4.1
Introduction
........................... 49
4.2
Histograms from Data Streams
................. 50
4.2.1
K-buckets Histograms
.................. 50
4.2.2
Exponential Histograms
................. 51
4.2.2.1
An Illustrative Example
............ 52
4.2.2.2
Discussion
................... 52
4.3
The Partition Incremental Discretization Algorithm
-
PiD
. . 53
4.3.1
Analysis of the Algorithm
................ 56
4.3.2
Change Detection in Histograms
............ 56
4.3.3
An Illustrative Example
................. 57
4.4
Applications to Data Mining
.................. 59
4.4.1
Applying PiD in Supervised Learning
.......... 59
4.4.2
Time-Changing Environments
.............. 61
4.5
Notes
............................... 62
5
Evaluating Streaming Algorithms
63
5.1
Introduction
........................... 63
5.2
Learning from Data Streams
.................. 64
5.3
Evaluation Issues
........................ 65
5.3.1
Design of Evaluation Experiments
........... 66
Contents
vii
5.3.2 Evaluation
Metrics
.................... 67
5.3.2.1
Error Estimators Using a Single Algorithm
and a Single
Dataset
.............. 68
5.3.2.2
An Illustrative Example
............ 68
5.3.3
Comparative Assessment
................ 69
5.3.3.1
The
0-1
Loss Function
........... 70
5.3.3.2
Illustrative Example
.............. 71
5.3.4
Evaluation Methodology in Non-Stationary
Environments
....................... 72
5.3.4.1
The Page-Hinkley Algorithm
......... 72
5.3.4.2
Illustrative Example
.............. 73
5.4
Lessons Learned and Open Issues
............... 75
5.5
Notes
............................... 77
Clustering from Data Streams
79
6.1
Introduction
........................... 79
6.2
Clustering Examples
....................... 80
6.2.1
Basic Concepts
...................... 80
6.2.2
Partitioning Clustering
.................. 82
6.2.2.1
The Leader Algorithm
............. 82
6.2.2.2
Single Pass k-Me&ns
.............. 82
6.2.3
Hierarchical Clustering
.................. 83
6.2.4
Micro Clustering
..................... 85
6.2.4.1
Discussion
................... 86
6.2.4.2
Monitoring Cluster Evolution
......... 86
6.2.5
Grid Clustering
...................... 87
6.2.5.1
Computing the Fractal Dimension
...... 88
6.2.5.2
Fractal Clustering
............... 88
6.3
Clustering Variables
....................... 90
6.3.1
A Hierarchical Approach
................. 91
6.3.1.1
Growing the Hierarchy
............ 91
6.3.1.2
Aggregating at Concept Drift Detection
... 94
6.3.1.3
Analysis of the Algorithm
........... 96
6.4
Notes
............................... 96
Frequent Pattern Mining
97
7.1
Introduction to Frequent Itemset Mining
........... 97
7.1.1
The Search Space
..................... 98
7.1.2
The FP-growth Algorithm
................ 100
7.1.3
Summarizing Itemsets
.................. 100
7.2
Heavy Hitters
.......................... 101
7.3
Mining Frequent Itemsets from Data Streams
......... 103
7.3.1
Landmark Windows
................... 104
7.3.1.1
The LossyCounting Algorithm
........ 104
7.3.1.2
Frequent Itemsets Using LossyCounting
. . 104
viii Knowledge Discovery
from
Data
Streams
7.3.2 Mining
Recent Frequent
Itemsets............ 105
7.3.2.1
Maintaining Frequent
Itemsets in
Sliding Win¬
dows
...................... 105
7.3.2.2
Mining Closed Frequent Itemsets over Sliding
Windows
.................... 106
7.3.3
Frequent Itemsets at Multiple Time Granularities
. . . 108
7.4
Sequence Pattern Mining
.................... 110
7.4.1
Reservoir Sampling for Sequential Pattern Mining over
Data Streams
.......................
Ill
7.5
Notes
............................... 113
8
Decision Trees from Data Streams
115
8.1
Introduction
........................... 115
8.2
The Very Fast Decision Tree Algorithm
............ 116
8.2.1
VFDT—The Base Algorithm
............... 116
8.2.2
Analysis of the VFDT Algorithm
............. 118
8.3
Extensions to the Basic Algorithm
............... 119
8.3.1
Processing Continuous Attributes
............ 119
8.3.1.1
Exhaustive Search
............... 119
8.3.1.2
Discriminant Analysis
............. 121
8.3.2
Functional Tree Leaves
.................. 123
8.3.3
Concept Drift
....................... 124
8.3.3.1
Detecting Changes
............... 126
8.3.3.2
Reacting to Changes
.............. 127
8.3.4
Final Comments
..................... 128
8.4
OLIN: Info-Fuzzy Algorithms
.................. 129
8.5
Notes
............................... 132
9
Novelty Detection in Data Streams
133
9.1
Introduction
........................... 133
9.2
Learning and Novelty
...................... 134
9.2.1
Desiderata for Novelty Detection
............ 135
9.3
Novelty Detection as a One-Class Classification Problem
. . 135
9.3.1
Autoassociator Networks
................ 136
9.3.2
The Positive Naive-Bayes
................ 137
9.3.3
Decision Trees for One-Class Classification
...... 138
9.3.4
The One-Class SVM
................... 138
9.3.5
Evaluation of One-Class Classification Algorithms
. . 139
9.4
Learning New Concepts
..................... 141
9.4.1
Approaches Based on Extreme Values
......... 141
9.4.2
Approaches Based on the Decision Structure
..... 142
9.4.3
Approaches Based on Frequency
............ 143
9.4.4
Approaches Based on Distances
............. 144
9.5
The Online Novelty and Drift Detection Algorithm
...... 144
9.5.1
Initial Learning Phase
.................. 145
Contents ix
9.5.2
Continuous Unsupervised Learning
Phase....... 146
9.5.2.1
Identifying Novel Concepts
.......... 147
9.5.2.2
Attempting to Determine the Nature of New
Concepts
.................... 149
9.5.2.3
Merging Similar Concepts
........... 149
9.5.2.4
Automatically Adapting the Number of Clus¬
ters
....................... 150
9.5.3
Computational Cost
................... 150
9.6
Notes
............................... 151
10
Ensembles of Classifiers
153
10.1
Introduction
........................... 153
10.2
Linear Combination of Ensembles
............... 155
10.3
Sampling from a Training Set
................. 156
10.3.1
Online Bagging
...................... 157
10.3.2
Online Boosting
..................... 158
10.4
Ensembles of Trees
....................... 160
10.4.1
Option Trees
....................... 160
10.4.2
Forest of Trees
...................... 161
10.4.2.1
Generating Forest of Trees
.......... 162
10.4.2.2
Classifying Test Examples
.......... 162
10.5
Adapting to Drift Using Ensembles of Classifiers
....... 162
10.6
Mining Skewed Data Streams with Ensembles
........ 165
10.7
Notes
............................... 166
11
Time Series Data Streams
167
11.1
Introduction to Time Series Analysis
............. 167
11.1.1
Trend
........................... 167
11.1.2
Seasonality
........................ 169
11.1.3
Stationarity
........................ 169
11.2
Time-Series Prediction
..................... 169
11.2.1
The
Kalman
Filter
.................... 170
11.2.2
Least Mean Squares
................... 173
11.2.3
Neural Nets and Data Streams
............. 173
11.2.3.1
Stochastic Sequential Learning of Neural Net¬
works
...................... 174
11.2.3.2
Illustrative Example: Load Forecast in Data
Streams
..................... 175
11.3
Similarity between Time-Series
................. 177
11.3.1
Euclidean Distance
.................... 177
11.3.2
Dynamic Time-Warping
................. 178
11.4
Symbolic Approximation
-
SAX
................. 180
11.4.1
The SAX Transform
.................... 180
11.4.1.1
Piecewise Aggregate Approximation (PAA)
. 181
11.4.1.2
Symbolic Discretization
............ 181
χ
Knowledge Discovery
from
Data
Streams
11.4.1.3
Distance Measure
............... 182
11.4.1.4
Discussion
................... 182
11.4.2
Finding Motifs Using SAX
................ 183
11.4.3
Finding Discords Using SAX
............... 183
11.5
Notes
............................... 184
12
Ubiquitous Data Mining
185
12.1
Introduction to Ubiquitous Data Mining
........... 185
12.2
Distributed Data Stream Monitoring
............. 186
12.2.1
Distributed Computing of Linear Functions
...... 187
12.2.1.1
A General Algorithm for Computing Linear
Functions
.................... 188
12.2.2
Computing Sparse Correlation Matrices Efficiently
. . 189
12.2.2.1
Monitoring Sparse Correlation Matrices
. . . 191
12.2.2.2
Detecting Significant Correlations
...... 192
12.2.2.3
Dealing with Data Streams
.......... 192
12.3
Distributed Clustering
...................... 193
12.3.1
Conquering the Divide
.................. 193
12.3.1.1
Furthest Point Clustering
........... 193
12.3.1.2
The Parallel Guessing Clustering
....... 193
12.3.2
DGClust
-
Distributed Grid Clustering
........ 194
12.3.2.1
Local Adaptive Grid
.............. 194
12.3.2.2
Frequent State Monitoring
.......... 195
12.3.2.3
Centralized Online Clustering
........ 196
12.4
Algorithm Granularity
..................... 197
12.4.1
Algorithm Granularity Overview
............ 199
12.4.2
Formalization of Algorithm Granularity
........ 200
12.4.2.1
Algorithm Granularity Procedure
...... 200
12.4.2.2
Algorithm Output Granularity
........ 201
12.5
Notes
............................... 203
13
Final Comments
205
13.1
The Next Generation of Knowledge Discovery
........ 205
13.1.1
Mining Spatial Data
................... 206
13.1.2
The Time Situation of Data
............... 206
13.1.3
Structured Data
..................... 206
13.2
Where We Want to Go
..................... 206
Appendix A Resources
209
A.I Software
............................. 209
A.
2
Datasets
............................. 209
Bibliography
211
Index
235
List of Tables
2.1
Comparison between Database Management Systems and Data
Stream Management Systems
................... 8
2.2
Differences between traditional and stream data query process¬
ing
.................................. 9
4.1
Average results of evaluation metrics of the quality of dis¬
cretization
............................. 58
5.1
Evaluation methods in stream mining literature
........ 66
5.2
Impact of fading factors in change detection
.......... 75
7.1
A transaction database and all possible frequent itemsets.
. . 98
7.2
The search space to find all possible frequent itemsets
..... 99
8.1
Contingency table to compute the entropy of a splitting test.
122
9.1
Confusion matrix to evaluate one-class classifiers
........ 139
11.1
The two time-series used in the example of dynamic time-
warping
............................... 178
11.2
SAX lookup table
.......................... 181
|
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| author_GND | (DE-588)130389420 |
| author_facet | Gama, João |
| author_role | aut |
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| id | DE-604.BV036090058 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:11:20Z |
| institution | BVB |
| isbn | 9781439826119 1439826110 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018980675 |
| oclc_num | 441142008 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM DE-473 DE-BY-UBG |
| owner_facet | DE-91 DE-BY-TUM DE-473 DE-BY-UBG |
| physical | XIX, 237 S. graph. Darst. |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Chapman & Hall |
| record_format | marc |
| series2 | Chapman & Hall/CRC data mining and knowledge discovery series |
| spelling | Gama, João Verfasser (DE-588)130389420 aut Knowledge discovery from data streams João Gama London Chapman & Hall 2010 XIX, 237 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC data mining and knowledge discovery series Data mining Machine learning Wissensextraktion (DE-588)4546354-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Data Mining (DE-588)4428654-5 s Wissensextraktion (DE-588)4546354-2 s Maschinelles Lernen (DE-588)4193754-5 s DE-604 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018980675&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Gama, João Knowledge discovery from data streams Data mining Machine learning Wissensextraktion (DE-588)4546354-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4546354-2 (DE-588)4193754-5 (DE-588)4428654-5 |
| title | Knowledge discovery from data streams |
| title_auth | Knowledge discovery from data streams |
| title_exact_search | Knowledge discovery from data streams |
| title_full | Knowledge discovery from data streams João Gama |
| title_fullStr | Knowledge discovery from data streams João Gama |
| title_full_unstemmed | Knowledge discovery from data streams João Gama |
| title_short | Knowledge discovery from data streams |
| title_sort | knowledge discovery from data streams |
| topic | Data mining Machine learning Wissensextraktion (DE-588)4546354-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Data mining Machine learning Wissensextraktion Maschinelles Lernen Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018980675&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT gamajoao knowledgediscoveryfromdatastreams |