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CROSSWORD
ANSWER

divisive

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The answer DIVISIVE has 13 possible clue(s) in existing crosswords.

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The word DIVISIVE is VALID in some board games. Check DIVISIVE in word games in Scrabble, Words With Friends, see scores, anagrams etc.

Searching in Dictionaries ...

Definitions of divisive in various dictionaries:

adj - dissenting (especially dissenting with the majority opinion)

Creating dissension or discord.

adj - causing disunity or dissension

Word Research / Anagrams and more ...

Keep reading for additional results and analysis below.

Possible Crossword Clues
Creating dissension
Tending to create dissension
Tending to cause disagreement within a group - in arithmetic way?
Tending to cause disagreement in group
Four's in the dive that's splitting
Nobleman with me in dive is causing disagreement between people
One's up after six, in disreputable club causing confict
Causing dissension
Tending to cause conflict
Causing disagreement

Last Seen in these Crosswords & Puzzles
Dec 31 2017 Premier Sunday - King Feature Syndicate
Sep 10 2014 The Telegraph - Cryptic
Aug 29 2012 Irish Times (Crosaire)
Mar 8 2011 The Guardian - Quick crossword
Nov 8 2009 The Times - Concise
Oct 6 2009 Irish Times (Simplex)
Dec 20 2007 Irish Times (Crosaire)
Sep 21 2006 The Times - Concise
Sep 28 2005 Irish Times (Simplex)
Jan 24 2005 New York Times

Possible Dictionary Clues
Creating dissension or discord.
tending to cause disagreement or hostility between people.
dissenting (especially dissenting with the majority opinion)
Tending to cause disagreement or hostility between people.
used to describe something that causes great and sometimes unfriendly disagreement within a group of people:
tending to cause disagreements that separate people into opposing groups:

Divisive might be related to
In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types: * Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. * Divisive: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram. * The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of * * * * * * O * * * ( * * n * * 3 * * * ) * * * {\displaystyle {\mathcal {O}}(n^{3})} * and requires * * * * * * O * * * ( * * n * * 2 * * * ) * * * {\displaystyle {\mathcal {O}}(n^{2})} * memory, which makes it too slow for even medium data sets. However, for some special cases, optimal efficient agglomerative methods (of complexity * * * * * * O * * * ( * * n * * 2 * * * ) * * * {\displaystyle {\mathcal {O}}(n^{2})} * ) are known: SLINK for single-linkage and CLINK for complete-linkage clustering. With a heap the runtime of the general case can be reduced to * * * * * * O * * * ( * * n * * 2 * * * log * ⁡ * n * ) * * * {\displaystyle {\mathcal {O}}(n^{2}\log n)} * at the cost of further increasing the memory requirements. In many programming languages, the memory overheads of this approach are too large to make it practically usable. * Except for the special case of single-linkage, none of the algorithms (except exhaustive search in * * * * * * O * * * ( * * 2 * * n * * * ) * * * {\displaystyle {\mathcal {O}}(2^{n})} * ) can be guaranteed to find the optimum solution. * Divisive clustering with an exhaustive search is * * * * * * O * * * ( * * 2 * * n * * * ) * * * {\displaystyle {\mathcal {O}}(2^{n})} * , but it is common to use faster heuristics to choose splits, such as k-means.

Divisive might be related to

In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:
* Agglomerative: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
* Divisive: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram.
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* , but it is common to use faster heuristics to choose splits, such as k-means.

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