An Information Theory based Approach to Multisource Clustering
Authors: Pierre-Alexandre Murena, Jérémie Sublime, Basarab Matei, Antoine Cornuéjols
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we propose a new algorithm based on solid theoretical basis, and test it on several real and artificial data sets. |
| Researcher Affiliation | Academia | 1 LTCI T el ecom Paris Tech, Paris, France 2 UMR MIA-Paris, Agro Paris Tech, INRA, Universit e Paris-Saclay, Paris, France 3 LISITE laboratory RDI team, ISEP, 10 rue de Vanves, Issy-les-Moulineaux, France 4 Universit e Paris 13 Sorbonne Paris Cit e, LIPN CNRS UMR 7030, Villetaneuse, France |
| Pseudocode | No | The paper describes the algorithm steps in paragraph form but does not include a formally structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code. |
| Open Datasets | Yes | The Wisconsin Data Breast Cancer (UCI): this data set contains 569 instances with 30 parameters and 2 classes. These 30 parameters contain 10 descriptors for 3 different cells (10 each) of the same patient. This data set can easily be split into 3 views: one for each cell. The Spam Base data set (UCI): The Spam Base data set contains 4601 observations described by 57 attributes and a label column: Spam or not Spam (1 or 0). The different attributes can be split into views containing word frequencies, letter frequencies and capital run sequences. The VHR Strasbourg data set [Rougier and Puissant, 2014]: it contains the description of 187058 segments extracted from a very high resolution satellite image of the French city of Strasbourg. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper mentions runtime and parallel computing but does not provide specific hardware details (like exact GPU/CPU models or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions various algorithms and models but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper describes the comparison setup and algorithm choices but does not provide specific experimental setup details such as hyperparameter values or detailed training configurations. |