A Proposed Clustering Algorithm for Efficient Clustering of High-Dimensional Data

Abstract

To partition transaction data values, clustering algorithms are used. To analyse the relationships between transactions, similarity measures are utilized. Similarity models based on vectors perform well with low-dimensional data. High-dimensional data values are clustered using subspace clustering techniques. Clustering high-dimensional data is difficult due to the curse of dimensionality. Projective clustering seeks out projected clusters in subsets of a data space's dimensions. In high-dimensional data space, a probability model represents predicted clusters. A model-based fuzzy projection clustering method to find clusters with overlapping boundaries in different projection subspaces. The system employs the Model Based Projective Clustering (MPC) method. To cluster high-dimensional data, projective clustering algorithms are used. A subspace clustering technique is the model-based projective clustering algorithm. Similarity analysis use non-axis-subspaces. Anomaly transactions are segmented using projected clusters. The suggested system is intended to cluster objects in high-dimensional spaces. The similarity analysis includes non-access subspaces. The clustering procedure validates anomaly data values with similarity. The subspace selection procedure has been optimized. A subspace clustering approach is the model-based projective clustering algorithm. Similarity analysis use non-axis-subspaces. Anomaly transactions are segmented using projected clusters. The suggested system is intended to cluster objects in high-dimensional spaces. The similarity analysis includes non-access subspaces. The clustering procedure validates anomaly data values with similarity. The subspace selection procedure has been improved.