Approximating Hierarchical MV-sets for Hierarchical Clustering
Authors: Assaf Glazer, Omer Weissbrod, Michael Lindenbaum, Shaul Markovitch
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present empirical results that demonstrate the superiority of our method over existing ones. |
| Researcher Affiliation | Academia | Assaf Glazer Omer Weissbrod Michael Lindenbaum Shaul Markovitch Department of Computer Science, Technion Israel Institute of Technology {assafgr,omerw,mic,shaulm}@cs.technion.ac.il |
| Pseudocode | No | The paper describes methods in prose but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | The olive oil dataset [10] consists of 572 olive oil examples, with 8 features each, from 3 regions in Italy (R1, R2, R3), each one further divided into 3 sub-areas. |
| Dataset Splits | Yes | We split the data X into two equal-sized train and test sets, and construct a tree using the train set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions specific methods and algorithms but does not list any software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | 20 i.i.d. points were sampled for training our q-OCSVM method, with α1 = 0.25, α2 = 0.5, α3 = 0.75 (3-quantiles), and with a bandwidth γ, which results in a cluster tree with 3 modes. |