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FcaKit

FcaKit is Swift library implementing Formal Concept Analysis data mining method. It works with macOS, iOS and Linux.

What is Formal Concept Analysis?

Formal concept analysis (FCA) is a principled way of deriving a concept hierarchy or formal ontology from a collection of objects and their properties. Each concept in the hierarchy represents the objects sharing some set of properties; and each sub-concept in the hierarchy represents a subset of the objects (as well as a superset of the properties) in the concepts above it. The term was introduced by Rudolf Wille in 1980, and builds on the mathematical theory of lattices and ordered sets that was developed by Garrett Birkhoff and others in the 1930s.

https://en.wikipedia.org/wiki/Formal_concept_analysis

More References

  • Ganter B., Wille R. Formal Concept Analysis. Mathematical Foundations. Springer, Berlin, 1999. ISBN 3-540-62771-5
  • Carpineto C., Romano G. Concept Data Analysis : Theory and Applications. John Wiley & Sons, 2004. ISBN 0-470-85055-8
  • Bělohlávek R., Introduction to Formal Concept Analysis, Olomouc 2008

Implemented Algorithms

  1. NextClosure
  2. UpperNeighbor
  3. Close by One (CbO)
  4. Fast Close by One (FCbO)
  5. Parallel Close by One (PCbO)
  6. Parallel Fast Close by One (PFCbO)
  7. ELL
  8. In-Close2
  9. In-Close4

Benchmarks

All dataset used in benchmark are located in FcaKit/datasets. Benchmarks were performed on MacBook Pro 2018 with Touchbar, CPU Intel Core i7 Quad core 2.7 GHz, 16 GB RAM. Each algortihm was run 100 times on the same dataset and final execution time is average of each execution.

Mushrooms Dataset

Size: 8124 x 119, 221525 concepts

| Algorithm | Time | |:-------------|:------------:| |Next Closure | 18.304s | |Close by One | 15.311s | |Fast Close by One | 5.393s | |Parallel Close by One | 10.286s | |Parallel Fast Close by One | 3.428s | |In-Close2 | 4.873s | |In-Close4 | 3.554s |

Cloud Dataset

Size: 108 x 56, 1030 concepts

| Algorithm | Time | |:-------------|:------------:| |Next Closure | 0.037s | |Upper Neighbor | 0.157s | |Closed by One | 0.033s | |Fast Close by One | 0.030s | |Parallel Closed by One | 0.097s | |Parallel Fast Closed by One | 0.047s | |ELL | 0.174s | |In-Close2 | 0.015s | |In-Close4 | 0.007s |

Wine Dataset

Size: 130 x 132, 5524 concepts

| Algorithm | Time | |:-------------|:------------:| |Next Closure | 0.439s | |Upper Neighbor | 1.306s | |Close by One | 0.498s | |Fast Close by One | 0.301s | |Parallel Close by One | 0.751s | |Parallel Fast Close by One | 0.372s | |ELL | 1.069s | |In-Close2 | 0.159s | |In-Close4 | 0.072s |

Panther Dataset

Size: 30x130, 1471 concepts

| Algorithm | Time | |:-------------|:------------:| |Next Closure | 0.123s | |Upper Neighbor | 0.083s | |Closed by One | 0.171s | |Fast Close by One | 0.069s | |Parallel Close by One | 0.226s | |Parallel Fast Close by One | 0.103s | |ELL | 0.066s | |In-Close2 | 0.044s | |In-Close4 | 0.026s |

Build

You can build FcaKit using Xcode. If you are Linux user you can use Swift Package Manager to build.

    swift build -c release

Add as Dependency

You can use FcaKit library in your swift package. Add FcaKit as dependency as in the following example.

    // swift-tools-version:5.1
    // The swift-tools-version declares the minimum version of Swift required to build this package.

    import PackageDescription

    let package = Package(
        name: "Application",
        platforms: [ .macOS(.v10_12), ], // This line is necessary. 
        dependencies: [
        .package(url: "https://github.com/rvyjidacek/FcaKit.git",  from: "1.0.0"),
        ],
        targets: [
            .target(
                name: "Application",
                dependencies: ["FcaKit"]), // Add FcaKit as dependecy here
            .testTarget(
                name: "ApplicationTests",
                dependencies: ["Application", "FcaKit"]), // Add FcaKit as dependecy here
        ]
    )

When you add dependency you have to update your package using:

    swift package update

This command builds FcaKit for release and executable binary you can find in .build/release/fca. Also you can user prepared script called build which builds FcaKit and move binary to current folder.

Usage

BitSet

Initialization

    // Initialize an empty bitset of given size. Size is maximal number which a set can contain. 
    let a = BitSet(size: 100)
    
    // You can also create a bitset with values
    BitSet(size: 100, values: [1, 2, 3, 5, 19]) // array
    BitSet(size: 100, values: 0..<50) // range        

Operations

    let a = BitSet(size: 10, values: [1, 2, 3, 4, 5])
    let b = BitSet(size: 10, values: 0..<10)
    
    // Intersection
    a.intersection(with: a)
    a &= b
    
    let c = a.intersected(b) 
    let d = a & b
    
    // Union
    a.union(with: a)
    a |= b
    
    let c = a.unioned(b) 
    let d = a | b
    
    // Difference
    a.difference(b)
    a -= b
    
    let c = a - b

Formal Context

Is tuple ⟨X, Y, I⟩ where X is set of object, Y is set of attributes and I ⊆ X ⨉ Y and if for x ∈ X and y ∈ Y we have ⟨x, y⟩ ∈ I we say that object x has and attibute y. In each formal context we have concept forming operators ↑ and ↓ where: for A ⊆ X and B ⊆ Y we have A↑ = { y ∈ Y | x ∈ A and ⟨x, y⟩ ∈ I } B↓ = { x ∈ X | y ∈ B and ⟨x, y⟩ ∈ I }

    // Initialization with values
    let context = FormalContext(values: [[1, 1, 1], [1, 0, 1], [0, 1, 1], [1, 0, 0]])
    
    // Get common attributes for objects 1 and 3
    let attributes = context.up(objects: BitSet(size: 4, values: [1, 3])) // {0}
    
    // Attributes for object 0
    let y = context.up(object: 0) // {0, 1, 2}
    
    // Get Get objects with attributes 2 and 3
    let objects = context.down(attributes: BitSet(size: 3, values: [2, 3])) // {0, 1, 2}
    
    // Objects with attribute 0
    let x = context.down(attribute: 0) // {0, 1}
    
    // Compute closure
    
    let closure1 = context.upAndDown(objects: BitSet(size: 4, values: [1, 3])) // {0, 1, 3}
    let closure2 = context.downAndUp(attributes: BitSet(size: 3, values: [2, 3])) // {2}

Formal Concept

Formal concept is 2-tuple ⟨A, B⟩ where A is set of objects and B is set of attributes and following holds A↑ = B and B↓ = A

Computing concepts

For computing concepts of some formal context we have many algorithms. Implemented algorithms can be found in section Implemented Algorithms. Super class of all agorithms is FcaAlgorithm and this class has method count. So you only crreate and Formal Context and pass this one to method count of some algorithm.

    let context = // Get formal context for example from csv file
    let algorithm = FCbO() // Create an instance of the algorithm
    
    // Compute concepts
    let concepts = algorithm.count(in: context)

Licence

Copyright (c) 2019 Roman Vyjidacek

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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