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Evaluation of Clustering Algorithms on Absenteeism at Work Dataset


Aarya Vardhan Reddy Paakaala , MVSR Engineering College; Sai Saran Macha, MVSR Engineering College; Kumara Saketh Mudigonda, MVSR Engineering College


Absenteeism At Work Dataset, Clustering, K- Means, Affinity Propagation, Hierarchical Clustering, DBSCAN


In our modern life, thousands of terabytes of data is created. Each tuple of the data has certain features which differentiate them from other tuples. In order to have efficient access and have a proper understanding of the data, we need to group them based on certain features. Clustering is a machine learning procedure which provides efficient algorithms for this purpose. In our paper, we have taken a real-world dataset named Absenteeism at work dataset. Our objective is to apply certain clustering algorithms on the real world dataset to understand the mechanism of the algorithms. This would help us group the real world modern data into certain clusters which would help us identify them effectively based on their features. We have used four clustering algorithms namely K- Means, Affinity Propagation, Hierarchical Clustering (Agglomerative), and DBSCAN.

Other Details

Paper ID: IJSRDV6I60243
Published in: Volume : 6, Issue : 6
Publication Date: 01/09/2018
Page(s): 337-342

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