hierarchical-clustering-0.4.7: Fast algorithms for single, average/UPGMA and complete linkage clustering.

Safe Haskell Safe Haskell98

Data.Clustering.Hierarchical.Internal.Types

Synopsis

# Documentation

data Dendrogram a Source #

Data structure for storing hierarchical clusters. The distance between clusters is stored on the branches. Distances between leafs are the distances between the elements on those leafs, while distances between branches are defined by the linkage used (see Linkage).

Constructors

 Leaf a The leaf contains the item a itself. Branch !Distance (Dendrogram a) (Dendrogram a) Each branch connects two clusters/dendrograms that are d distance apart.
Instances
 Source # Does not recalculate the distances! Instance details Methodsfmap :: (a -> b) -> Dendrogram a -> Dendrogram b #(<\$) :: a -> Dendrogram b -> Dendrogram a # Source # Instance details Methodsfold :: Monoid m => Dendrogram m -> m #foldMap :: Monoid m => (a -> m) -> Dendrogram a -> m #foldr :: (a -> b -> b) -> b -> Dendrogram a -> b #foldr' :: (a -> b -> b) -> b -> Dendrogram a -> b #foldl :: (b -> a -> b) -> b -> Dendrogram a -> b #foldl' :: (b -> a -> b) -> b -> Dendrogram a -> b #foldr1 :: (a -> a -> a) -> Dendrogram a -> a #foldl1 :: (a -> a -> a) -> Dendrogram a -> a #toList :: Dendrogram a -> [a] #null :: Dendrogram a -> Bool #length :: Dendrogram a -> Int #elem :: Eq a => a -> Dendrogram a -> Bool #maximum :: Ord a => Dendrogram a -> a #minimum :: Ord a => Dendrogram a -> a #sum :: Num a => Dendrogram a -> a #product :: Num a => Dendrogram a -> a # Source # Instance details Methodstraverse :: Applicative f => (a -> f b) -> Dendrogram a -> f (Dendrogram b) #sequenceA :: Applicative f => Dendrogram (f a) -> f (Dendrogram a) #mapM :: Monad m => (a -> m b) -> Dendrogram a -> m (Dendrogram b) #sequence :: Monad m => Dendrogram (m a) -> m (Dendrogram a) # Eq a => Eq (Dendrogram a) Source # Instance details Methods(==) :: Dendrogram a -> Dendrogram a -> Bool #(/=) :: Dendrogram a -> Dendrogram a -> Bool # Ord a => Ord (Dendrogram a) Source # Instance details Methodscompare :: Dendrogram a -> Dendrogram a -> Ordering #(<) :: Dendrogram a -> Dendrogram a -> Bool #(<=) :: Dendrogram a -> Dendrogram a -> Bool #(>) :: Dendrogram a -> Dendrogram a -> Bool #(>=) :: Dendrogram a -> Dendrogram a -> Bool #max :: Dendrogram a -> Dendrogram a -> Dendrogram a #min :: Dendrogram a -> Dendrogram a -> Dendrogram a # Show a => Show (Dendrogram a) Source # Instance details MethodsshowsPrec :: Int -> Dendrogram a -> ShowS #show :: Dendrogram a -> String #showList :: [Dendrogram a] -> ShowS #

data Linkage Source #

The linkage type determines how the distance between clusters will be calculated. These are the linkage types currently available on this library.

Constructors

 SingleLinkage The distance between two clusters a and b is the minimum distance between an element of a and an element of b. CompleteLinkage The distance between two clusters a and b is the maximum distance between an element of a and an element of b. CLINK The same as CompleteLinkage, but using the CLINK algorithm. It's much faster however doesn't always give the best complete linkage dendrogram. UPGMA Unweighted Pair Group Method with Arithmetic mean, also called "average linkage". The distance between two clusters a and b is the arithmetic average between the distances of all elements in a to all elements in b. FakeAverageLinkage This method is usually wrongly called "average linkage". The distance between cluster a = a1 U a2 (that is, cluster a was formed by the linkage of clusters a1 and a2) and an old cluster b is (d(a1,b) + d(a2,b)) / 2. So when clustering two elements to create a cluster, this method is the same as UPGMA. However, in general when joining two clusters this method assigns equal weights to a1 and a2, while UPGMA assigns weights proportional to the number of elements in each cluster. See, for example:http://www.cs.tau.ac.il/~rshamir/algmb/00/scribe00/html/lec08/node21.html, which defines the real UPGMA and gives the equation to calculate the distance between an old and a new cluster.http://github.com/JadeFerret/ai4r/blob/master/lib/ai4r/clusterers/average_linkage.rb, code for "average linkage" on ai4r library implementing what we call here FakeAverageLinkage and not UPGMA.
Instances
 Source # Instance details MethodsenumFrom :: Linkage -> [Linkage] #enumFromThen :: Linkage -> Linkage -> [Linkage] #enumFromTo :: Linkage -> Linkage -> [Linkage] #enumFromThenTo :: Linkage -> Linkage -> Linkage -> [Linkage] # Source # Instance details Methods(==) :: Linkage -> Linkage -> Bool #(/=) :: Linkage -> Linkage -> Bool # Source # Instance details Methods(<) :: Linkage -> Linkage -> Bool #(<=) :: Linkage -> Linkage -> Bool #(>) :: Linkage -> Linkage -> Bool #(>=) :: Linkage -> Linkage -> Bool # Source # Instance details MethodsshowList :: [Linkage] -> ShowS #

A distance is simply a synonym of Double for efficiency.