A local search algorithm for k-means with outliers
k-Means is a well-studied clustering problem that finds applications in many fields related to
unsupervised learning. It is known that k-means clustering is highly sensitive to the isolated …
unsupervised learning. It is known that k-means clustering is highly sensitive to the isolated …
Heterogeneous network model to infer human disease-long intergenic non-coding RNA associations
With the knowledge of molecular biology it is highlighting that long non-coding RNAs (lncRNAs)
play a critical role in many important biological processes, such as imprinting control, …
play a critical role in many important biological processes, such as imprinting control, …
Fast algorithms for distributed k-clustering with outliers
In this paper, we study the $ k $-clustering problems with outliers in distributed setting. The
current best results for the distributed $ k $-center problem with outliers have quadratic local …
current best results for the distributed $ k $-center problem with outliers have quadratic local …
An Improved Approximation Algorithm for the k-Means Problem with Penalties
The clustering problem has been paid lots of attention in various fields of compute science.
However, in many applications, the existence of noisy data poses a big challenge for the …
However, in many applications, the existence of noisy data poses a big challenge for the …
PTAS for Minimum Cost MultiCovering with Disks
In this paper, we study the following Minimum Cost Multicovering (MCMC) problem: Given a
set of \(n\) client points \(C\) and a set of \(m\) server points \(S\) in a fixed dimensional \(\…
set of \(n\) client points \(C\) and a set of \(m\) server points \(S\) in a fixed dimensional \(\…
A unified framework of FPT approximation algorithms for clustering problems
In this paper, we present a framework for designing FPT approximation algorithms for many
k-clustering problems. Our results are based on a new technique for reducing search spaces…
k-clustering problems. Our results are based on a new technique for reducing search spaces…
Improved PTAS for the constrained k-means problem
The k-means problem has been paid lots of attention in many fields, and each cluster of the
k-means problem always satisfies locality property. In this paper, we study the constrained k-…
k-means problem always satisfies locality property. In this paper, we study the constrained k-…
[PDF][PDF] Improved algorithms for clustering with outliers
Clustering is a fundamental problem in computer science and finds applications in a wide
42 range of domains. Depending on the objective function, it has many different variants. …
42 range of domains. Depending on the objective function, it has many different variants. …
Synthesis of dodecylbenzene via the alkylation of benzene and 1‐dodecene over mesopore Beta zeolites
The alkylation of benzene with long chain α‐olefins is a crucial process in the production of
fine chemicals, which requires the upgrading from traditional homogeneous catalysis to …
fine chemicals, which requires the upgrading from traditional homogeneous catalysis to …
Randomized parameterized algorithms for -Packing and Co-Path Packing problems
In this paper, we study the Parameterized $$P_2$$ P 2 -Packing problem and Parameterized
Co-Path Packing problem from random perspective. For the Parameterized $$P_2$$ P 2 -…
Co-Path Packing problem from random perspective. For the Parameterized $$P_2$$ P 2 -…