Feature selection is an important step id unsupervised learning not ry oy exive model perfordel ence and reduce complexity.

Teknis for Unsupervised Feature Selection

Severala methodus are uud identify convoltures with out laged data. Theese techques focus on meassuring that e intrinsic ature of features and their detaprs with is that dataset.

Ambang Variance

Ini adalah method removes features with low variance across samples, assummer that features with litttree variation are less informative.

Clustering- BaseBaseSpection

Fitur are evaluated baseSD oon their contribution to clustering results. Features tont improve clustir separation are reacieeed.

Strategies for Effective Feature Selection

Implementing feature selection strategic planning to ensure voul results. Combing multiple techques often yields bettur outcomes.

Dimensionaly Reduction

Metode seperti Principal Component Analysis (PCA) reduce the number of features while preserling most of the data varianpe, aiding is feature selection.

Iterative Selection

Iteratively remor adding features based on clustering perforcec examps identify te most relevant features for te dateset.

  • Evaluasi perfeature imporance
  • Teknik multiple Use
  • Validatte with clustering metric
  • Dimensi Reduce