Data Mining: Concepts and Techniques
E566635
Data Mining: Concepts and Techniques is a widely used academic textbook that systematically introduces the principles, algorithms, and practical methods of data mining and knowledge discovery from large datasets.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Data Mining: Concepts and Techniques canonical | 5 |
How this entity was disambiguated
This entity first appeared as the object of triple T6082259 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Data Mining: Concepts and Techniques Context triple: [Jeffrey D. Ullman, notableWork, Data Mining: Concepts and Techniques]
-
A.
IEEE International Conference on Data Mining
The IEEE International Conference on Data Mining is a leading annual research conference that focuses on advances in data mining, machine learning, and knowledge discovery in databases.
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B.
ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data mining, knowledge discovery, and related areas of data science and machine learning.
-
C.
KDD
KDD is the commonly used abbreviation for Norway’s Ministry of Local Government and Regional Development, a government body responsible for municipal affairs, regional policy, and housing.
-
D.
ACM Transactions on Data Science
ACM Transactions on Data Science is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data science, including theory, methods, and applications.
-
E.
SIGKDD
SIGKDD is the ACM Special Interest Group on Knowledge Discovery and Data Mining, best known for its flagship KDD conference and contributions to data mining and machine learning research.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Data Mining: Concepts and Techniques Target entity description: Data Mining: Concepts and Techniques is a widely used academic textbook that systematically introduces the principles, algorithms, and practical methods of data mining and knowledge discovery from large datasets.
-
A.
IEEE International Conference on Data Mining
The IEEE International Conference on Data Mining is a leading annual research conference that focuses on advances in data mining, machine learning, and knowledge discovery in databases.
-
B.
ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data mining, knowledge discovery, and related areas of data science and machine learning.
-
C.
KDD
KDD is the commonly used abbreviation for Norway’s Ministry of Local Government and Regional Development, a government body responsible for municipal affairs, regional policy, and housing.
-
D.
ACM Transactions on Data Science
ACM Transactions on Data Science is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data science, including theory, methods, and applications.
-
E.
SIGKDD
SIGKDD is the ACM Special Interest Group on Knowledge Discovery and Data Mining, best known for its flagship KDD conference and contributions to data mining and machine learning research.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
academic textbook
ⓘ
computer science textbook ⓘ non-fiction book ⓘ |
| author |
Jian Pei
NERFINISHED
ⓘ
Jiawei Han NERFINISHED ⓘ Micheline Kamber NERFINISHED ⓘ |
| countryOfPublication |
United States of America
ⓘ
surface form:
United States
|
| feature |
algorithm pseudocode
ⓘ
case studies ⓘ exercises at the end of chapters ⓘ worked examples ⓘ |
| field |
data mining
ⓘ
database systems ⓘ knowledge discovery in databases ⓘ machine learning ⓘ |
| firstEditionPublicationYear | 2000 ⓘ |
| hasEdition |
first edition of Data Mining: Concepts and Techniques
ⓘ
second edition of Data Mining: Concepts and Techniques ⓘ third edition of Data Mining: Concepts and Techniques ⓘ |
| intendedAudience |
data mining practitioners
ⓘ
graduate students ⓘ researchers ⓘ undergraduate students ⓘ |
| language | English ⓘ |
| publisher | Morgan Kaufmann NERFINISHED ⓘ |
| secondEditionPublicationYear | 2006 ⓘ |
| structure |
chapters on advanced topics and applications
ⓘ
chapters on data preprocessing and data warehousing ⓘ chapters on major data mining tasks ⓘ introductory chapters on data mining concepts ⓘ |
| thirdEditionPublicationYear | 2011 ⓘ |
| topic |
OLAP
ⓘ
association rule mining ⓘ classification ⓘ cluster analysis ⓘ data cube technology ⓘ data mining applications ⓘ data mining methodologies ⓘ data preprocessing ⓘ data warehousing ⓘ frequent pattern mining ⓘ outlier detection ⓘ prediction ⓘ spatial data mining ⓘ text mining ⓘ web mining ⓘ |
| usedAs | university course textbook ⓘ |
| usesAlgorithmType |
association analysis algorithms
ⓘ
classification algorithms ⓘ clustering algorithms ⓘ outlier detection algorithms ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Data Mining: Concepts and Techniques Description of subject: Data Mining: Concepts and Techniques is a widely used academic textbook that systematically introduces the principles, algorithms, and practical methods of data mining and knowledge discovery from large datasets.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.