Browse
Publications
Preprints
About
About UCL Open: Env.
Aims and Scope
Editorial Board
Indexing
APCs
How to cite
Publishing policies
Editorial policy
Peer review policy
Equality, Diversity & Inclusion
About UCL Press
Contact us
For authors
Information for authors
How it works
Benefits of publishing with us
Submit
How to submit
Preparing your manuscript
Article types
Open Data
ORCID
APCs
Contributor agreement
For reviewers
Information for reviewers
Review process
How to peer review
Peer review policy
My ScienceOpen
Sign in
Register
Dashboard
Search
Browse
Publications
Preprints
About
About UCL Open: Env.
Aims and Scope
Editorial Board
Indexing
APCs
How to cite
Publishing policies
Editorial policy
Peer review policy
Equality, Diversity & Inclusion
About UCL Press
Contact us
For authors
Information for authors
How it works
Benefits of publishing with us
Submit
How to submit
Preparing your manuscript
Article types
Open Data
ORCID
APCs
Contributor agreement
For reviewers
Information for reviewers
Review process
How to peer review
Peer review policy
My ScienceOpen
Sign in
Register
Dashboard
Search
26
views
0
references
Top references
cited by
0
Cite as...
0 reviews
Review
0
comments
Comment
0
recommends
+1
Recommend
0
collections
Add to
0
shares
Share
Twitter
Sina Weibo
Facebook
Email
1,626
similar
All similar
Record
: found
Abstract
: not found
Book
: not found
Principles of Data Mining and Knowledge Discovery
other
Editor(s):
Tapio Elomaa
,
Heikki Mannila
,
Hannu Toivonen
Publication date
(Online):
September 18 2002
Publisher:
Springer Berlin Heidelberg
Read this book at
Publisher
Buy book
Review
Review book
Invite someone to review
Bookmark
Cite as...
There is no author summary for this book yet. Authors can add summaries to their books on ScienceOpen to make them more accessible to a non-specialist audience.
Related collections
The FAIR data principles
Author and book information
Book
ISBN (Print):
978-3-540-44037-6
ISBN (Electronic):
978-3-540-45681-0
Publication date (Print):
2002
Publication date (Online):
September 18 2002
DOI:
10.1007/3-540-45681-3
SO-VID:
a52e3183-c507-435e-bfe7-17fdbd044b7d
History
Data availability:
Comments
Comment on this book
Sign in to comment
Book chapters
pp. 62
The Need for Low Bias Algorithms in Classification Learning from Large Data Sets
pp. 325
Geography of Di.erences between Two Classes of Data
pp. 410
Answering the Most Correlated N Association Rules Efficiently
pp. 51
On the Discovery of Weak Periodicities in Large Time Series
pp. 74
Mining All Non-derivable Frequent Itemsets
pp. 125
A Classification Approach for Prediction of Target Events in Temporal Sequences
pp. 275
Spatial Subgroup Mining Integrated in an Object-Relational Spatial Database
pp. 348
Clustering Ontology-Based Metadata in the Semantic Web
pp. 373
SVM Classification Using Sequences of Phonemes and Syllables
Similar content
1,626
Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques
Authors:
Raza Hasan
,
Sellappan Palaniappan
,
Salman Mahmood
…
Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine
Authors:
Rezarta Islamaj Doğan
,
Sun Kim
,
Andrew Chatr-aryamontri
…
Identification of Thiotetronic Acid Antibiotic Biosynthetic Pathways by Target-directed Genome Mining.
Authors:
Xiaoyu Tang
,
Jie Li
,
Natalie Millán-Aguiñaga
…
See all similar