A Proposed Clustering Algorithm for Efficient Clustering of High-Dimensional Data
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| Titel: | A Proposed Clustering Algorithm for Efficient Clustering of High-Dimensional Data |
|---|---|
| Autoren: | S. Gopinath, G. Kowsalya, K Sakthivel, S. Arularasi |
| Quelle: | Journal of Information Technology and Cryptography. 1:14-21 |
| Verlagsinformationen: | QTanalytics India (Publications), 2023. |
| Publikationsjahr: | 2023 |
| Beschreibung: | 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. |
| Publikationsart: | Article |
| DOI: | 10.48001/joitc.2023.1114-21 |
| Dokumentencode: | edsair.doi...........cfd6e7a31cea826b39cde42eea23ae11 |
| Datenbank: | OpenAIRE |
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