Lloyd’s algorithm
E426673
Lloyd’s algorithm is an iterative clustering method that partitions data into k groups by repeatedly assigning points to the nearest cluster center and updating those centers to minimize within-cluster variance.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Lloyd’s algorithm canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4277256 — 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: Lloyd’s algorithm Context triple: [KMeans, alsoKnownAs, Lloyd’s algorithm]
-
A.
Huffman
Huffman is a surname most commonly associated with the American computer scientist David A. Huffman, known for developing Huffman coding in information theory and data compression.
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B.
Thompson's algorithm
Thompson's algorithm is a classic computer science method for converting regular expressions into nondeterministic finite automata (NFAs), widely used in pattern matching and lexical analysis.
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C.
Marzullo's algorithm
Marzullo's algorithm is a method for selecting the most likely correct time interval from multiple, possibly conflicting time sources, commonly used in clock synchronization systems.
-
D.
Scott encoding
Scott encoding is a method in lambda calculus for representing algebraic data types and their pattern matching behavior using higher-order functions.
-
E.
Golomb
Golomb is a station on the Carmelit underground funicular system in Haifa, Israel.
- 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: Lloyd’s algorithm Target entity description: Lloyd’s algorithm is an iterative clustering method that partitions data into k groups by repeatedly assigning points to the nearest cluster center and updating those centers to minimize within-cluster variance.
-
A.
Huffman
Huffman is a surname most commonly associated with the American computer scientist David A. Huffman, known for developing Huffman coding in information theory and data compression.
-
B.
Thompson's algorithm
Thompson's algorithm is a classic computer science method for converting regular expressions into nondeterministic finite automata (NFAs), widely used in pattern matching and lexical analysis.
-
C.
Marzullo's algorithm
Marzullo's algorithm is a method for selecting the most likely correct time interval from multiple, possibly conflicting time sources, commonly used in clock synchronization systems.
-
D.
Scott encoding
Scott encoding is a method in lambda calculus for representing algebraic data types and their pattern matching behavior using higher-order functions.
-
E.
Golomb
Golomb is a station on the Carmelit underground funicular system in Haifa, Israel.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
clustering algorithm
ⓘ
iterative optimization method ⓘ unsupervised learning method ⓘ |
| alsoKnownAs |
Lloyd–Forgy algorithm
ⓘ
linked to:
KMeans
standard k-means algorithm ⓘ
linked to:
KMeans
|
| assignmentStepDescription | assign each point to the nearest cluster center ⓘ |
| assumes | Euclidean distance metric by default ⓘ |
| canUse | other distance metrics with modifications ⓘ |
| category | k-means methods ⓘ |
| commonInitializationMethod |
Forgy method
ⓘ
k-means++ initialization ⓘ random selection of initial centers ⓘ |
| convergesWhen |
change in objective function is below a threshold
ⓘ
cluster assignments no longer change ⓘ |
| doesNotGuarantee | global optimum ⓘ |
| field |
information theory
ⓘ
machine learning ⓘ statistics ⓘ |
| firstPublishedIn | 1982 ⓘ |
| guarantees | convergence to a local minimum of the objective function ⓘ |
| hasStep |
assignment step
ⓘ
convergence check ⓘ initialization of k cluster centers ⓘ update step ⓘ |
| input |
number of clusters k
ⓘ
set of data points ⓘ |
| limitation |
may converge to poor local minima
ⓘ
requires pre-specifying number of clusters k ⓘ sensitive to outliers and noise ⓘ |
| minimizes |
sum of squared distances to cluster centers
ⓘ
within-cluster variance ⓘ |
| objectiveFunction | within-cluster sum of squares ⓘ |
| optimizationType | local optimization ⓘ |
| originalApplicationDomain | pulse-code modulation ⓘ |
| output |
cluster assignments for each data point
ⓘ
k cluster centers ⓘ |
| proposedBy | Stuart P. Lloyd ⓘ |
| proposedIn | 1957 ⓘ |
| relatedTo |
Forgy algorithm
ⓘ
expectation–maximization algorithm ⓘ k-means++ ⓘ
linked to:
KMeans
|
| sensitiveTo | initialization of cluster centers ⓘ |
| timeComplexity | O(n k d i) ⓘ |
| timeComplexityDescription | n data points, k clusters, d dimensions, i iterations ⓘ |
| typicalUseCase |
clustering in data mining
ⓘ
image compression ⓘ signal quantization ⓘ |
| updateStepDescription | recompute each cluster center as the mean of its assigned points ⓘ |
| usedFor |
k-means clustering
ⓘ
partitioning data into k clusters ⓘ vector quantization ⓘ |
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: Lloyd’s algorithm Description of subject: Lloyd’s algorithm is an iterative clustering method that partitions data into k groups by repeatedly assigning points to the nearest cluster center and updating those centers to minimize within-cluster variance.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.