-- Initial fastcluster.cabal generated by cabal init. For further -- documentation, see http://haskell.org/cabal/users-guide/ name: clustering version: 0.2.0 synopsis: High performance clustering algorithms description: Following clutering methods are included in this library: . 1 Agglomerative hierarchical clustering. Complete linkage O(n^2), Single linkage O(n^2), Average linkage O(n^2), Weighted linkage O(n^2), Ward's linkage O(n^2). . 2 KMeans clustering. license: MIT license-file: LICENSE author: Kai Zhang maintainer: kai@kzhang.org copyright: (c) 2015 Kai Zhang category: Math build-type: Simple -- extra-source-files: cabal-version: >=1.10 library exposed-modules: AI.Clustering.Hierarchical AI.Clustering.Hierarchical.Internal AI.Clustering.Hierarchical.Types AI.Clustering.KMeans AI.Clustering.KMeans.Internal AI.Clustering.KMeans.Types -- other-modules: build-depends: base >=4.0 && <5.0 , binary , containers , matrices >=0.4.0 , mwc-random , parallel , primitive , vector hs-source-dirs: src ghc-options: -Wall default-language: Haskell2010 test-suite test type: exitcode-stdio-1.0 hs-source-dirs: tests main-is: test.hs other-modules: Test.Hierarchical Test.KMeans Test.Utils default-language: Haskell2010 build-depends: base , binary , mwc-random , matrices , vector , tasty , tasty-hunit , tasty-quickcheck , clustering , hierarchical-clustering , split , Rlang-QQ benchmark bench type: exitcode-stdio-1.0 hs-source-dirs: benchmarks ghc-options: -threaded -rtsopts -with-rtsopts=-N2 main-is: bench.hs other-modules: Bench.Hierarchical Bench.KMeans Bench.Utils default-language: Haskell2010 build-depends: base , criterion , mwc-random , vector , clustering , hierarchical-clustering , matrices source-repository head type: git location: https://github.com/kaizhang/clustering.git