Hierarchical clustering
method of cluster analysis which seeks to build a hierarchy of clusters
In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories:
Agglomerative: Agglomerative clustering, often referred to as a "bottom-up" approach, begins with each data point as an individual cluster. At each step, the algorithm merges the two most similar clusters based on a chosen distance metric (e.g., Euclidean distance) and linkage criterion (e.g., single-linkage, complete-linkage). This process continues until all data points are combined into a single cluster or a stopping criterion is met. Agglomerative methods are more commonly used due to their simplicity and computational efficiency for small to medium-sized datasets.
Divisive: Divisive clustering, known as a "top-down" approach, starts with all data points in a single cluster and recursively splits the cluster into smaller ones. At each step, the algorithm selects a cluster and divides it into two or more subsets, often using a criterion such as maximizing the distance between resulting clusters. Divisive methods are less common but can be useful when the goal is to identify large, distinct clusters first.
In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram.
The public source identifies “Hierarchical clustering” as method of cluster analysis which seeks to build a hierarchy of clusters. This brief keeps that definition visible, then builds a research path around Hierarchical, clustering and method.
Why this record matters
A short description can identify a subject without explaining its stakes. For “Hierarchical clustering”, the useful work is to connect “method of cluster analysis which seeks to build a hierarchy of clusters” to the records capable of establishing context and consequence.
Vocabulary and entity names are the principal evidence signals here, because they determine the precision of every later search. The source revision retrieved here is dated Aug 12, 2026. The linked authority identifier is Q1277447. The Library of Congress control number is sh2013002984. 1 of 1 selected statements include explicit references; 0 carry qualifiers and 0 use preferred rank.
A concise general-reference account can conceal disagreements about scope, terminology or the weight assigned to individual sources. The lead is largely declarative, so disagreement and counter-evidence require a deliberate search beyond the opening account. Authority statements aid reconciliation but still require their own references, qualifiers and ranks to be checked.
How to read it
Use the entry as an orientation point, then follow its citations and revision history. Names, dates and institutional relationships should be checked against the original record.
- Subject orientation
- Search vocabulary
- Locating named sources
The closest primary source, responsible institution and strongest cited specialist reference.
Three-step research path
- Establish the record: confirm the title “Hierarchical clustering”, its source revision and the description used here.
- Expand the search: follow Hierarchical clustering primary sources, Hierarchical clustering archive and Hierarchical research across catalogues and specialist indexes.
- Test the account: compare the strongest cited source with the responsible institution’s current record and note any disagreement.
Questions for further research
- Which source most directly establishes the central claim about “Hierarchical clustering”?
- Which cited source is closest to the event, object or claim?
- Which institution is responsible for the underlying evidence?
Search terms from this dossier
This entry incorporates text from “Hierarchical clustering” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.