Mining Convex Polygon Patterns with Formal Concept Analysis
Authors: Aimene Belfodil, Sergei O. Kuznetsov, Céline Robardet, Mehdi Kaytoue
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our contribution is threefold: (i) We formally introduce such patterns in Formal Concept Analysis (FCA), (ii) we give all the basic bricks for mining convex polygons with exhaustive search and pattern sampling, and (iii) we design several algorithms, which we compare experimentally. ... We report an experimental study of the different algorithms, carried out on a machine equipped with Intel Core i72600 CPUs 3.4 Ghz machine with 16 GB RAM. |
| Researcher Affiliation | Collaboration | Aimene Belfodil1,2, Sergei O. Kuznetsov3, C eline Robardet1, Mehdi Kaytoue1 1Univ Lyon, INSA Lyon, CNRS, LIRIS UMR 5205, F-69621, Lyon, France 2Mobile Devices Ingenierie, 100 Avenue Stalingrad, 94800, Villejuif, France 3National Research University Higher School of Economics, Moscow, Russia |
| Pseudocode | Yes | Algorithm 1 EXTCBO; Algorithm 2 DELAUNAYENUM; Algorithm 3 EXTREMEPOINTSENUM |
| Open Source Code | Yes | All materials are available on https://github.com/BelfodilAimene/MiningConvexPolygonPatterns. |
| Open Datasets | Yes | Datasets consist of n objects drawn from the IRIS dataset uniformly from the three different classes for the attributes sepal-length and sepal-width (or petal-lentgh and petal-width). ... [Falher et al., 2015] propose several spatial datasets (cities) containing Foursquare users check-in places of different kinds (...). We randomly choose the city of Saint-Louis (Missouri, USA) containing 3464 points. |
| Dataset Splits | No | The paper mentions the IRIS and Foursquare datasets but does not provide specific details on how they were split into training, validation, or test sets, nor does it specify cross-validation methods. |
| Hardware Specification | Yes | We report an experimental study of the different algorithms, carried out on a machine equipped with Intel Core i72600 CPUs 3.4 Ghz machine with 16 GB RAM. |
| Software Dependencies | No | Algorithms are implemented in Java. The paper does not specify the Java version or any specific libraries with version numbers used for the implementation. |
| Experiment Setup | No | The paper describes the algorithms and the types of constraints varied in the experiments (e.g., 'Max Shape complexity', 'Min Support'), and mentions 'maximum budget of 20K iterations' for MCTS, but it does not provide specific numerical values for hyperparameters, training configurations, or detailed settings for the various algorithms or models. |