CLTD-LP: un enfoque optimizado de agrupamiento de arriba hacia abajo con árboles de prefijos lineales para el

M Sinthuja1, M Diviya2, P Saranya2

  • 1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India. sinthuja.m@vit.ac.in.

Scientific reports
|February 19, 2026
PubMed
Resumen

Este estudio presenta un nuevo enfoque de agrupamiento de arriba hacia abajo (CLTDLP) para la minería eficiente de conjuntos de artículos frecuentes. El algoritmo CLTDLP reduce significativamente el tiempo de ejecución y el uso de memoria en comparación con los métodos existentes.

Videos de Conceptos Relacionados

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
5.9K
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
9.5K
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.1K