Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
April 24, 2026International Journal of Engineering & TechnologyOpen Access

Enhanced Feature Selection Clustering Algorithm for Attribute Similarity in High Dimensional Data

View Full Paper
Ask AI
Bookmark
Share

Authors

DMDeena Babu MandruDr. Reddy's Laboratories (India)YKY. K. Sundara Krishna

Discussion

Loading...

Member takes

Overview

This algorithm improves attribute similarity in high dimensional data, indicating better feature selection efficiency.

Key Points

  • The goal is to enhance feature selection in clustering to improve data presentation in high dimensional datasets.
  • Proposed Enhanced Feature Selection based Clustering (EFSC) algorithm with two implementation stages.
  • First stage: Classify features into clusters using a graph-based theoretical approach.
  • Second stage: Identify the most representative attributes from each cluster for feature subsets.
  • EFSC yields a smaller subset of features with higher accuracy.
  • Demonstrates improved time efficiency for real-time datasets.
  • Outperforms existing algorithms like FCBF, ReliefF, and CFS in selected classifiers.

Cite This Study

Mandru et al. (2018) studied this question.

synapsesocial.com/papers/69eb084f553a5433e34b35dchttps://doi.org/10.14419/ijet.v7i4.29.21641
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Feature Selection in High Dimension Datasets using Incremental Feature Clustering2024
  2. 2Dynamic Optimistic Ensemble Clustered Feature Selection Techniques for Big Data Analysis2025
  3. 3Enhanced Features extraction method based on Fuzzy C-meansalgorithm2025
  4. 4Enhanced Features extraction methodbased on Fuzzy C-meansalgorithm2025
  5. 5Feature Selection With Discernibility and Independence Criteria2024 · 8 citations