Unidimensional Clustering of Discrete Data Using Latent Tree Models
Authors: April Liu, Leonard Poon, Nevin Zhang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive empirical studies have been conducted to compare the new method with LCM and several other methods (K-means, kernel Kmeans and spectral clustering) that are not model-based. |
| Researcher Affiliation | Academia | 1 Department of Computer Science and Engineering The Hong Kong University of Science and Technology, Hong Kong {aprillh, lzhang}@cse.ust.hk 2 Department of Mathematics and Information Technology The Hong Kong Institute of Education, Hong Kong kmpoon@ied.edu.hk |
| Pseudocode | Yes | Algorithm 1 shows the pseudo-code for our algorithm. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | The real-world data sets were from the UCI machine learning repository. |
| Dataset Splits | No | The paper describes a process for learning LCMs where cardinality is gradually increased and parameters re-estimated until the model score ceases to increase (guided by AIC/BIC). While this acts as a form of model selection/validation, it does not specify explicit train/validation dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions methods like EM algorithm and algorithms from other papers but does not specify software names with version numbers for dependencies (e.g., Python, PyTorch, scikit-learn versions). |
| Experiment Setup | Yes | In our experiments, the threshold δ is set at 3 as suggested by Kass and Raftery (1995)... To do so, we initially set the cardinality of Y1 at 2 and optimized the probability parameters using the EM algorithm... Then the cardinality is gradually increased and the parameters are re-estimated after each increase. The process stops when model score ceases to increase. |