84 citations · 171 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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,…
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…