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O que (quem) é fuzzy algorithm - definição
CLUSTER ANALYSIS WHERE MEMBERSHIP OF A DATA POINT IN A CLUSTER IS FUZZY
Soft K-means; Fuzzy C-means clustering; FCM algorithm; Applications of fuzzy clustering
Approximate string matching
ALGORITHM FOR FINDING STRINGS THAT MATCH A PATTERN APPROXIMATELY
Fuzzy string searching; Fuzzy search; Fuzzy searching; Fuzzy string matching; Fuzzy finder; Approximate substring matching; Approximately matching strings; Fzf; FZF
In computer science, approximate string matching (often colloquially referred to as fuzzy string searching) is the technique of finding strings that match a pattern approximately (rather than exactly). The problem of approximate string matching is typically divided into two sub-problems: finding approximate substring matches inside a given string and finding dictionary strings that match the pattern approximately.
fuzzy subset
SETS WHOSE ELEMENTS HAVE DEGREES OF MEMBERSHIP
Fuzzy sets; Fuzzy set theory; Fuzzification; Fuzzy subset; Credibility(fuzzy); Fuzzy category; Goguen category; Fuzzy Sets; Fuzzy relation equation; Pythagorean fuzzy set; Degree of membership; Uncertain set
In fuzzy logic, a fuzzy subset F of a set S is defined by a
"membership function" which gives the degree of membership of
each element of S belonging to F.
Prim's algorithm
ALGORITHM
Jarnik algorithm; Prim-Jarnik algorithm; Prim-Jarnik's algorithm; Jarnik's algorithm; Prim-Jarník; DJP algorithm; Jarník algorithm; Jarník's algorithm; Jarníks algorithm; Jarniks algorithm; Prim-Jarník algorithm; Prim-Jarnik; Prim algorithm; Prim’s algorithm; Jarník-Prim; Prims algorithm
In computer science, Prim's algorithm (also known as Jarník's algorithm) is a greedy algorithm that finds a minimum spanning tree for a weighted undirected graph. This means it finds a subset of the edges that forms a tree that includes every vertex, where the total weight of all the edges in the tree is minimized.
Wikipédia
Fuzzy clustering
Fuzzy clustering (also referred to as soft clustering or soft k-means) is a form of clustering in which each data point can belong to more than one cluster.
Clustering or cluster analysis involves assigning data points to clusters such that items in the same cluster are as similar as possible, while items belonging to different clusters are as dissimilar as possible. Clusters are identified via similarity measures. These similarity measures include distance, connectivity, and intensity. Different similarity measures may be chosen based on the data or the application.
