LOF: Identifying Density-Based Local Outliers
E1196393
UNEXPLORED
"LOF: Identifying Density-Based Local Outliers" is a seminal data mining paper that introduced the Local Outlier Factor (LOF) algorithm for detecting anomalous data points based on local density deviations.
All labels observed (1)
| Label | Occurrences |
|---|---|
| LOF: Identifying Density-Based Local Outliers canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T16136050 — 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: LOF: Identifying Density-Based Local Outliers Context triple: [Hans-Peter Kriegel, notableWork, LOF: Identifying Density-Based Local Outliers]
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A.
Outlier Analysis
Outlier Analysis is a comprehensive book by Charu C. Aggarwal that systematically covers the theory, algorithms, and applications of detecting anomalous data in various domains.
-
B.
Mining of Massive Datasets
"Mining of Massive Datasets" is a widely used textbook that introduces practical and scalable data mining and machine learning techniques for analyzing large-scale datasets.
-
C.
Top 10 algorithms in data mining
"Top 10 algorithms in data mining" is a widely cited survey paper that summarizes and evaluates the most influential data mining algorithms across key tasks such as classification, clustering, and association analysis.
-
D.
Data Mining: Concepts and Techniques
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.
-
E.
Data Mining: The Textbook
Data Mining: The Textbook is a comprehensive academic book that systematically covers the principles, algorithms, and applications of data mining and knowledge discovery in databases.
- 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: LOF: Identifying Density-Based Local Outliers Target entity description: "LOF: Identifying Density-Based Local Outliers" is a seminal data mining paper that introduced the Local Outlier Factor (LOF) algorithm for detecting anomalous data points based on local density deviations.
-
A.
Outlier Analysis
Outlier Analysis is a comprehensive book by Charu C. Aggarwal that systematically covers the theory, algorithms, and applications of detecting anomalous data in various domains.
-
B.
Mining of Massive Datasets
"Mining of Massive Datasets" is a widely used textbook that introduces practical and scalable data mining and machine learning techniques for analyzing large-scale datasets.
-
C.
Top 10 algorithms in data mining
"Top 10 algorithms in data mining" is a widely cited survey paper that summarizes and evaluates the most influential data mining algorithms across key tasks such as classification, clustering, and association analysis.
-
D.
Data Mining: Concepts and Techniques
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.
-
E.
Data Mining: The Textbook
Data Mining: The Textbook is a comprehensive academic book that systematically covers the principles, algorithms, and applications of data mining and knowledge discovery in databases.
- F. None of above. chosen
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.