Affinity Propagation with Background Knowledge using Pairwise Constraints
|International Journal of Computer Science and Engineering|
|© 2017 by SSRG - IJCSE Journal|
|Volume 4 Issue 2|
|Year of Publication : 2017|
|Authors : Saravanakumar.R, Dr.C.Nandini|
How to Cite?
Saravanakumar.R, Dr.C.Nandini, "Affinity Propagation with Background Knowledge using Pairwise Constraints," SSRG International Journal of Computer Science and Engineering , vol. 4, no. 2, pp. 1-3 , 2017. Crossref, https://doi.org/10.14445/23488387/IJCSE-V4I2P101
Data mining is the process of identifying and extracting hidden patterns and information from large databases and warehouses. Incorporating pairwise constraints into clustering algorithms is an emerging research area for machine learning and data mining communities. Already various algorithms exist to combine relative similarities between clusters from different viewpoints. But they suffer from duplicates in clusters and also lesser relevancy. The proposed Affinity propagation clustering algorithm uses semi-supervised learning to avoid data redundancy from input strings and ensures quicker retrieval. Final Clusters contain unique and relevant data. Semi-supervised learning falls between unsupervised learning (without any label training data) and supervised learning (with completely labelled training data). Thus the hybrid algorithms provides performance enhancement over its existing counterparts. Further large amount of input data can be processed precisely and even various alternative forms of similar output data can be retrieved. Hence the highest degree of accuracy can be achieved in clustering data and retrieval of the same by the improved affinity propagation algorithm.
Affinity propagation, clusters, pairwise constraints, semi-supervised learning.
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