Welcome to Anagrammer Crossword Genius! Keep reading below to see if neatest is an answer to any crossword puzzle or word game (Scrabble, Words With Friends etc). Scroll down to see all the info we have compiled on neatest.
Searching in Crosswords ...
The answer NEATEST has 79 possible clue(s) in existing crosswords.
Searching in Word Games ...
The word NEATEST is VALID in some board games. Check NEATEST in word games in Scrabble, Words With Friends, see scores, anagrams etc.
Searching in Dictionaries ...
Definitions of neatest in various dictionaries:
adj - clean or organized
adj - showing care in execution
adj - free from what is tawdry or unbecoming
Word Research / Anagrams and more ...
Keep reading for additional results and analysis below.
|Possible Crossword Clues|
|Like prizewinning handwriting|
|Working best, as a trick|
|Most like Felix Unger|
|Least in need of a cleanup|
|Clever to the max|
|Possible Dictionary Clues|
|superlative form of neat: most neat.|
|arranged in a tidy way in good order.|
|done with or demonstrating skill or efficiency.|
|(of liquid, especially spirits) not diluted or mixed with anything else.|
|Neatest might refer to|
|Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values. Formally, the nearest-neighbor (NN) search problem is defined as follows: given a set S of points in a space M and a query point q ∈ M, find the closest point in S to q. Donald Knuth in vol. 3 of The Art of Computer Programming (1973) called it the post-office problem, referring to an application of assigning to a residence the nearest post office. A direct generalization of this problem is a k-NN search, where we need to find the k closest points.|
* Most commonly M is a metric space and dissimilarity is expressed as a distance metric, which is symmetric and satisfies the triangle inequality. Even more common, M is taken to be the d-dimensional vector space where dissimilarity is measured using the Euclidean distance, Manhattan distance or other distance metric. However, the dissimilarity function can be arbitrary. One example are asymmetric Bregman divergences, for which the triangle inequality does not hold.