5 papers
Data-Native Global Optimization for Big Data K-means Clustering
Ravil Mussabayev, Rustam Mussabayev, Zukhra Yerdaliyeva +1
Big data clustering remains challenging: the Minimum Sum-of-Squares Clustering (MSSC) problem underlying K-means is NP-hard, and existing methods either reach poor local minima or…
MLLM-Microscope: Unlocking Hidden Structure Within Multimodal Large Language Models
Ravil Mussabayev, Rustam Mussabayev
This work presents MLLM-Microscope, a novel system designed for analyzing the hidden representations within Multimodal Large Language Models (MLLMs). Our system evaluates the linea…
Boosting K-means for Big Data by Fusing Data Streaming with Global Optimization
Ravil Mussabayev, Rustam Mussabayev
K-means clustering is a cornerstone of data mining, but its efficiency deteriorates when confronted with massive datasets. To address this limitation, we propose a novel heuristic…
Variable Landscape Search: A Novel Metaheuristic Paradigm for Unlocking Hidden Dimensions in Global Optimization
Rustam Mussabayev, Ravil Mussabayev
This paper presents the Variable Landscape Search (VLS), a novel metaheuristic designed to globally optimize complex problems by dynamically altering the objective function landsca…
WRDScore: New Metric for Evaluation of Natural Language Generation Models
Ravil Mussabayev
Evaluating natural language generation models, particularly for method name prediction, poses significant challenges. A robust metric must account for the versatility of method nam…