84 citations · 171 across the 11 of their papers we have counts for
11 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…
Superior Parallel Big Data Clustering through Competitive Stochastic Sample Size Optimization in Big-means
Rustam Mussabayev, Ravil Mussabayev
This paper introduces a novel K-means clustering algorithm, an advancement on the conventional Big-means methodology. The proposed method efficiently integrates parallel processing…
Finetuning Large Language Models for Vulnerability Detection
Alexey Shestov, Rodion Levichev, Ravil Mussabayev +3
This paper presents the results of finetuning large language models (LLMs) for the task of detecting vulnerabilities in source code. We leverage WizardCoder, a recent improvement o…
High-Performance Hybrid Algorithm for Minimum Sum-of-Squares Clustering of Infinitely Tall Data
Ravil Mussabayev, Rustam Mussabayev
This paper introduces a novel formulation of the clustering problem, namely the Minimum Sum-of-Squares Clustering of Infinitely Tall Data (MSSC-ITD), and presents HPClust, an innov…
Comparative Analysis of Optimization Strategies for K-means Clustering in Big Data Contexts: A Review
Ravil Mussabayev, Rustam Mussabayev
This paper presents a comparative analysis of different optimization techniques for the K-means algorithm in the context of big data. K-means is a widely used clustering algorithm,…
Structure-Aware Code Vulnerability Analysis With Graph Neural Networks
Ravil Mussabayev
This study explores the effectiveness of graph neural networks (GNNs) for vulnerability detection in software code, utilizing a real-world dataset of Java vulnerability-fixing comm…
How to Use K-means for Big Data Clustering?
Rustam Mussabayev, Nenad Mladenovic, Bassem Jarboui +1
K-means plays a vital role in data mining and is the simplest and most widely used algorithm under the Euclidean Minimum Sum-of-Squares Clustering (MSSC) model. However, its perfor…